Triple

T24323586
Position Surface form Disambiguated ID Type / Status
Subject UEFA Champions League Final 2020 E613035 entity
Predicate decidingGoalScorer P2220 FINISHED
Object Kingsley Coman
Kingsley Coman is a French professional footballer, primarily a winger, known for his pace, dribbling, and success with top European clubs including Bayern Munich and the French national team.
E1627101 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: Kingsley Coman | Statement: [UEFA Champions League Final 2020, decidingGoalScorer, Kingsley Coman]
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: Kingsley Coman
Triple: [UEFA Champions League Final 2020, decidingGoalScorer, Kingsley Coman]
Generated description
Kingsley Coman is a French professional footballer, primarily a winger, known for his pace, dribbling, and success with top European clubs including Bayern Munich and the French national team.

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_69e2d7db6d5c819091194918157a7c1f completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f292ae127881909ef1772278181ce3 completed April 29, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc9e4a1748190b5637b682458124a completed May 22, 2026, 3:13 a.m.
NEDg Description generation batch_6a0fcade9db88190b79f8f03c9b5f51f completed May 22, 2026, 3:17 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcb724a888190838a30e05e556421 completed May 22, 2026, 3:20 a.m.
Created at: April 18, 2026, 1:53 a.m.