Triple

T23891139
Position Surface form Disambiguated ID Type / Status
Subject Tait E600770 entity
Predicate hasNotableBearer P458 FINISHED
Object Hugh Tait
Hugh Tait was a prominent British museum curator and scholar of glass and decorative arts, best known for his long association with the British Museum and his influential publications on glass history.
 E1660314 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: Hugh Tait | Statement: [Tait, hasNotableBearer, Hugh Tait]
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: Hugh Tait
Triple: [Tait, hasNotableBearer, Hugh Tait]
Generated description
Hugh Tait was a prominent British museum curator and scholar of glass and decorative arts, best known for his long association with the British Museum and his influential publications on glass history.

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_69e295341ac0819080647f2908af793c completed April 17, 2026, 8:16 p.m.
NER Named-entity recognition batch_69f1cd036dd48190be508063b18762a4 completed April 29, 2026, 9:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a104863b4d081909d57f287dfa38032 completed May 22, 2026, 12:13 p.m.
NEDg Description generation batch_6a10492e43f881908cff348a5057993d completed May 22, 2026, 12:16 p.m.
NED2 Entity disambiguation (via description) batch_6a1049f506d88190a495098f5dac33c2 completed May 22, 2026, 12:20 p.m.
Created at: April 17, 2026, 8:25 p.m.