Sentence Segmentation Issue with spacy_llm and en_core_web_trf

**Description:**

I'm encountering a problem with sentence segmentation when integrating spacy_llm components into a spaCy pipeline that is based on en_core_web_trf.

**Observed Behavior:**

- Sentence segmentation fails when spacy_llm components are added to the pipeline.
- The issue does not occur when using spacy_llm components in a blank pipeline.

**Environment:**

Using the latest versions of spaCy and en_core_web_trf.

- **Config File (Example):**

```
[paths]
examples = "examples.json"

[nlp]
lang = "en"
pipeline = ["transformer", "tagger", "parser", "lemmatizer", "llm", "llm_rel"]

[components]

[components.transformer]
source = "en_core_web_trf"

[components.tagger]
source = "en_core_web_trf"

[components.parser]
source = "en_core_web_trf"

[components.lemmatizer]
source = "en_core_web_trf"

[components.llm]
factory = "llm"

[components.llm.task]
@llm_tasks = "spacy.NER.v3"
labels = ["DISH", "INGREDIENT", "EQUIPMENT", "PERSON", "LOCATION"]
description = "Entities are the names food dishes,
ingredients, and any kind of cooking equipment.
Adjectives, verbs, adverbs are not entities.
Pronouns are not entities."

[components.llm.task.label_definitions]
DISH = "Known food dishes, e.g. Lobster Ravioli, garlic bread"
INGREDIENT = "Individual parts of a food dish, including herbs and spices."
EQUIPMENT = "Any kind of cooking equipment. e.g. oven, cooking pot, grill"

[components.llm.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "examples.json"

[components.llm.model]
@llm_models = "spacy.Ollama.3.1.8b"

[components.llm_rel]
factory = "llm_rel"

[components.llm_rel.task]
@llm_tasks = "spacy.REL.v1"
labels = LivesIn,Visits

[components.llm_rel.task.examples]
@misc = "spacy.FewShotReader.v1"
path = "examples.jsonl"

[components.llm_rel.model]
@llm_models = "spacy.Ollama.3.1.8b"

```

**Steps to Reproduce:**

1. Load the en_core_web_trf pipeline with the modified config.
2. Process a text with the modified pipeline.
3. Observe the lack of sentence segmentation.

**Troubleshooting:**

- Tried explicitly adding sentencizer to the pipeline.
- Experimented with different component orders.
- Verified config loading process.

If i run this code:

```
` self.nlp = spacy.load('en_core_web_trf')

self.nlp = assemble(config_path=self.config_path, overrides={"paths.examples": str(self.examples_path)})

print("config: ", self.nlp.config.to_str())

print("PIPELINE: ", self.nlp.pipeline)
`
```

- It gives me the following pipeline configuration:

`IPELINE: [('transformer', ), ('tagger', ), ('parser', ), ('lemmatizer', ), ('llm', ), ('llm_rel', )]`

But while processing text, it gives me the following error:

`ValueError: [E030] Sentence boundaries unset. You can add the 'sentencizer' component to the pipeline with: `nlp.add_pipe('sentencizer')`. Alternatively, add the dependency parser or sentence recognizer, or set sentence boundaries by setting `doc[i].is_sent_start`.`

I would appreciate any guidance or assistance in resolving this issue. Thank you!