Triple

T27268593
Position Surface form Disambiguated ID Type / Status
Subject Minister of Fuel and Power of the United Kingdom E687980 entity
Predicate positionHeldBy P8 FINISHED
Object David Eccles
David Eccles was a British Conservative politician and government minister who held several key posts in the mid-20th century, including roles in economic and educational policy.
E1762968 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: David Eccles | Statement: [Minister of Fuel and Power of the United Kingdom, positionHeldBy, David Eccles]
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: David Eccles
Triple: [Minister of Fuel and Power of the United Kingdom, positionHeldBy, David Eccles]
Generated description
David Eccles was a British Conservative politician and government minister who held several key posts in the mid-20th century, including roles in economic and educational policy.

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_69ef3557abc481908bf3c146f0f3356a completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f626f529548190ba59773be80922a0 completed May 2, 2026, 4:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12628b6a148190879f74e1ac22ffc8 completed May 24, 2026, 2:29 a.m.
NEDg Description generation batch_6a1266920d008190b029acd1c8efc214 completed May 24, 2026, 2:46 a.m.
NED2 Entity disambiguation (via description) batch_6a1266f0b7448190a158f776016efacd completed May 24, 2026, 2:48 a.m.
Created at: April 27, 2026, 10:57 a.m.