Triple

T25044295
Position Surface form Disambiguated ID Type / Status
Subject France Davis Cup team E627191 entity
Predicate notablePlayer P304 FINISHED
Object Henri Leconte
Henri Leconte is a former French professional tennis player known for his powerful left-handed game, flair on court, and key role in France’s Davis Cup successes in the 1980s and early 1990s.
E2290304 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: Henri Leconte | Statement: [France Davis Cup team, notablePlayer, Henri Leconte]
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: Henri Leconte
Triple: [France Davis Cup team, notablePlayer, Henri Leconte]
Generated description
Henri Leconte is a former French professional tennis player known for his powerful left-handed game, flair on court, and key role in France’s Davis Cup successes in the 1980s and early 1990s.

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_69e2ff2b4c80819087c916b2b16241b9 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f4530f26948190af6de16b9013815f completed May 1, 2026, 7:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5bb8b220708190ad2529f0cccfc84c completed July 18, 2026, 5:32 p.m.
NEDg Description generation batch_6a5bb9244da48190a18b7242f6726a0c completed July 18, 2026, 5:34 p.m.
NED2 Entity disambiguation (via description) batch_6a5bb9742c148190a71d68dc439c9081 completed July 18, 2026, 5:35 p.m.
Created at: April 18, 2026, 6:08 a.m.