Triple

T33320262
Position Surface form Disambiguated ID Type / Status
Subject Stiefel–Whitney classes E853116 entity
Predicate introducedBy P513 FINISHED
Object Eduard Stiefel
Eduard Stiefel was a Swiss mathematician known for his contributions to algebraic topology and numerical analysis, including co-introducing the Stiefel–Whitney characteristic classes.
E2045034 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: Eduard Stiefel | Statement: [Stiefel–Whitney classes, introducedBy, Eduard Stiefel]
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: Eduard Stiefel
Triple: [Stiefel–Whitney classes, introducedBy, Eduard Stiefel]
Generated description
Eduard Stiefel was a Swiss mathematician known for his contributions to algebraic topology and numerical analysis, including co-introducing the Stiefel–Whitney characteristic classes.

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_69f349685f088190b8fda44083a018a9 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6df0e61308190bba135cc42c404cc completed May 3, 2026, 5:37 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3543321c908190ab0eeb737cbfd02e completed June 19, 2026, 1:25 p.m.
NEDg Description generation batch_6a3543e43dc8819091abfb05f7e4d523 completed June 19, 2026, 1:28 p.m.
NED2 Entity disambiguation (via description) batch_6a354505ffd481908b3bc40d99401aa0 completed June 19, 2026, 1:32 p.m.
Created at: May 1, 2026, 1:33 a.m.