Triple

T27293686
Position Surface form Disambiguated ID Type / Status
Subject Long Island Jewish Medical Center E688698 entity
Predicate hasCampus P116 FINISHED
Object Zucker Hillside Hospital
Zucker Hillside Hospital is a renowned psychiatric and behavioral health facility in Queens, New York, known for its research and treatment in mental health care.
 E1764003 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: Zucker Hillside Hospital | Statement: [Long Island Jewish Medical Center, hasCampus, Zucker Hillside 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: Zucker Hillside Hospital
Triple: [Long Island Jewish Medical Center, hasCampus, Zucker Hillside Hospital]
Generated description
Zucker Hillside Hospital is a renowned psychiatric and behavioral health facility in Queens, New York, known for its research and treatment in mental health care.

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_69ef355a96308190a2bed991525fb278 completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f6277e806c819085dbcbddb9d86af1 completed May 2, 2026, 4:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12629f87848190a01bdaedbc4c77b2 completed May 24, 2026, 2:29 a.m.
NEDg Description generation batch_6a126403e5488190bf5ed5d998adbd59 completed May 24, 2026, 2:35 a.m.
NED2 Entity disambiguation (via description) batch_6a126492b6bc819081d728b8a12d501e completed May 24, 2026, 2:38 a.m.
Created at: April 27, 2026, 11:17 a.m.