Triple

T24354762

Position	Surface form	Disambiguated ID	Type / Status
Subject	Gevher Nesibe Museum of Medical History	`E613890`	entity
Predicate	namedAfter	`P63`	FINISHED
Object	Gevher Nesibe Gevher Nesibe was a Seljuk-era princess renowned for her patronage of medicine and the founding of one of the earliest medical schools and hospitals in Anatolia.	`E1642647`	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: Gevher Nesibe | Statement: [Gevher Nesibe Museum of Medical History, namedAfter, Gevher Nesibe]

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: Gevher Nesibe
Triple: [Gevher Nesibe Museum of Medical History, namedAfter, Gevher Nesibe]

Generated description

Gevher Nesibe was a Seljuk-era princess renowned for her patronage of medicine and the founding of one of the earliest medical schools and hospitals in Anatolia.

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_69e2d7ddd29481909e7f539a6072bd71`	completed	April 18, 2026, 1:01 a.m.
NER	Named-entity recognition	`batch_69f29347e86881909cfbe5f23ce538b9`	completed	April 29, 2026, 11:24 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a0ff83d2b788190a8245c509ff84cbc`	completed	May 22, 2026, 6:31 a.m.
NEDg	Description generation	`batch_6a0ff9f4d2b881908e438b3436480274`	completed	May 22, 2026, 6:38 a.m.
NED2	Entity disambiguation (via description)	`batch_6a0ffae6e5bc81908af60c2c5d9eaa23`	completed	May 22, 2026, 6:42 a.m.

Created at: April 18, 2026, 1:59 a.m.