Triple

T25955830

Position	Surface form	Disambiguated ID	Type / Status
Subject	Ankara State Theatre	`E654096`	entity
Predicate	operatedBy	`P86`	FINISHED
Object	Turkish State Theatres Turkish State Theatres is Turkey’s national network of publicly funded repertory theatres responsible for producing and promoting stage performances across the country.	`E654096`	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: Turkish State Theatres | Statement: [Ankara State Theatre, operatedBy, Turkish State Theatres]

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: Turkish State Theatres
Triple: [Ankara State Theatre, operatedBy, Turkish State Theatres]

Generated description

Turkish State Theatres is Turkey’s national network of publicly funded repertory theatres responsible for producing and promoting stage performances across the country.

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_69e7ab40ac788190a771bc499eb1ae5f`	completed	April 21, 2026, 4:52 p.m.
NER	Named-entity recognition	`batch_69f6049ef41c81908bace173136c2b00`	completed	May 2, 2026, 2:05 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a110779fa8c81909f6864e47d928cb3`	completed	May 23, 2026, 1:48 a.m.
NEDg	Description generation	`batch_6a1108ea3754819081686ac7fa8f8e1b`	completed	May 23, 2026, 1:54 a.m.
NED2	Entity disambiguation (via description)	`batch_6a1109785d80819093c4602d784e1485`	completed	May 23, 2026, 1:57 a.m.

Created at: April 22, 2026, 8:44 a.m.