Triple

T24422706
Position Surface form Disambiguated ID Type / Status
Subject France vs Mexico (1930 FIFA World Cup) E615766 entity
Predicate goalScorerForFrance P2695 FINISHED
Object André Maschinot
André Maschinot was a French footballer best known for scoring twice in France’s opening match of the inaugural 1930 FIFA World Cup.
E2289476 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: André Maschinot | Statement: [France vs Mexico (1930 FIFA World Cup), goalScorerForFrance, André Maschinot]
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: André Maschinot
Triple: [France vs Mexico (1930 FIFA World Cup), goalScorerForFrance, André Maschinot]
Generated description
André Maschinot was a French footballer best known for scoring twice in France’s opening match of the inaugural 1930 FIFA World Cup.

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_69e2d7eadb248190a867130fe45f0388 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f2a6d73e208190873ab97996fd6b28 completed April 30, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5b425656248190bac0b8c3600364d3 completed July 18, 2026, 9:07 a.m.
NEDg Description generation batch_6a5b42d10ae0819085c4a7b16e6f94f2 completed July 18, 2026, 9:09 a.m.
NED2 Entity disambiguation (via description) batch_6a5b4306fe58819095a7fe7e30ec146f completed July 18, 2026, 9:10 a.m.
Created at: April 18, 2026, 2:14 a.m.