Triple
T24050029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lifespan health system |
E595628
|
entity |
| Predicate | operates |
P24
|
FINISHED |
| Object |
Bradley Hospital
Bradley Hospital is a psychiatric hospital in Rhode Island specializing in mental health care for children and adolescents.
|
E1625229
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Bradley Hospital | Statement: [Lifespan health system, operates, Bradley Hospital]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bradley Hospital Triple: [Lifespan health system, operates, Bradley Hospital]
Generated description
Bradley Hospital is a psychiatric hospital in Rhode Island specializing in mental health care for children and adolescents.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e288c184b081909f1f1751fb8e299a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d9cfb914819091a3378518f9f28d |
completed | April 29, 2026, 10:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0fbcfa05f081909f9102c31c21a8fb |
completed | May 22, 2026, 2:18 a.m. |
| NEDg | Description generation | batch_6a0fc03e594c8190aad5eed4e6006ffc |
completed | May 22, 2026, 2:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0fc15d85ec8190841005d247ad01f3 |
completed | May 22, 2026, 2:37 a.m. |
Created at: April 17, 2026, 10:19 p.m.