Triple

T28704338
Position Surface form Disambiguated ID Type / Status
Subject Stephen Coppell E729644 entity
Predicate nameInEnglish P3437 FINISHED
Object Stephen Coppell
Stephen Coppell is an English former professional footballer and manager, best known for his playing career with Manchester United and his successful managerial spells at clubs such as Reading and Crystal Palace.
E1846203 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: Stephen Coppell | Statement: [Stephen Coppell, nameInEnglish, Stephen Coppell]
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: Stephen Coppell
Triple: [Stephen Coppell, nameInEnglish, Stephen Coppell]
Generated description
Stephen Coppell is an English former professional footballer and manager, best known for his playing career with Manchester United and his successful managerial spells at clubs such as Reading and Crystal Palace.

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_69f043e6e9688190b6bdd6e5665498ff completed April 28, 2026, 5:21 a.m.
NER Named-entity recognition batch_69f656b6d94c8190ab3d7530603f53e9 completed May 2, 2026, 7:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25058fc2b08190a6d11785d5e9fd2b completed June 7, 2026, 5:45 a.m.
NEDg Description generation batch_6a250a0727108190bc085f034870e22e completed June 7, 2026, 6:04 a.m.
NED2 Entity disambiguation (via description) batch_6a250f52bc788190a19327674f832883 completed June 7, 2026, 6:27 a.m.
Created at: April 28, 2026, 5:44 a.m.