Triple

T24313922
Position Surface form Disambiguated ID Type / Status
Subject Gale–Nikaidō–Debreu theorem E612750 entity
Predicate namedAfter P63 FINISHED
Object Hirofumi Nikaidō
Hirofumi Nikaidō was a Japanese economist and mathematician known for his contributions to general equilibrium theory and mathematical economics.
E2296781 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: Hirofumi Nikaidō | Statement: [Gale–Nikaidō–Debreu theorem, namedAfter, Hirofumi Nikaidō]
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: Hirofumi Nikaidō
Triple: [Gale–Nikaidō–Debreu theorem, namedAfter, Hirofumi Nikaidō]
Generated description
Hirofumi Nikaidō was a Japanese economist and mathematician known for his contributions to general equilibrium theory and mathematical economics.

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_69e2d7da491c8190b6e6218af50923db completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f292a569dc81908971a4026e612a05 completed April 29, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a82b867fd4c81909b951f0337da44c0 completed Aug. 17, 2026, 7:29 a.m.
NEDg Description generation batch_6a82b94cdf208190b7f0263b67e41148 completed Aug. 17, 2026, 7:33 a.m.
NED2 Entity disambiguation (via description) batch_6a82b99e5f40819082e9b2a2d55e0485 completed Aug. 17, 2026, 7:34 a.m.
Created at: April 18, 2026, 1:45 a.m.