Triple

T27463396

Position	Surface form	Disambiguated ID	Type / Status
Subject	Assistance publique – Hôpitaux de Paris	`E693112`	entity
Predicate	hasPart	`P35`	FINISHED
Object	Hôpital Henri-Mondor Hôpital Henri-Mondor is a major teaching and research hospital in the Paris region, known for its advanced medical care and specialized clinical services.	`E1813694`	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: Hôpital Henri-Mondor | Statement: [Assistance publique – Hôpitaux de Paris, hasPart, Hôpital Henri-Mondor]

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: Hôpital Henri-Mondor
Triple: [Assistance publique – Hôpitaux de Paris, hasPart, Hôpital Henri-Mondor]

Generated description

Hôpital Henri-Mondor is a major teaching and research hospital in the Paris region, known for its advanced medical care and specialized clinical services.

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_69ef538105548190a771cc5a0cf8c211`	completed	April 27, 2026, 12:16 p.m.
NER	Named-entity recognition	`batch_69f62dfb084881909cdf5ac0324d1f92`	completed	May 2, 2026, 5:01 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a162784aefc8190b9366edb98c602f0`	completed	May 26, 2026, 11:06 p.m.
NEDg	Description generation	`batch_6a1628f366d88190b10dda8b0ab63762`	completed	May 26, 2026, 11:12 p.m.
NED2	Entity disambiguation (via description)	`batch_6a16297370d08190a0088aa14476bb1f`	completed	May 26, 2026, 11:14 p.m.

Created at: April 27, 2026, 12:51 p.m.