Triple

T12888465
Position Surface form Disambiguated ID Type / Status
Subject Clarence Kolb E308293 entity
Predicate name P16 FINISHED
Object Clarence Kolb E308293 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: Clarence Kolb | Statement: [Clarence Kolb, name, Clarence Kolb]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Clarence Kolb
Context triple: [Clarence Kolb, name, Clarence Kolb]
  • A. Clarence Kolb chosen
    Clarence Kolb was an American vaudeville and film actor best known for his comic character roles in Hollywood movies of the 1930s and 1940s.
  • B. George L. Dahl
    George L. Dahl was a prominent 20th-century American architect known for shaping much of Dallas’s skyline and major civic landmarks.
  • C. Clarence Peters
    Clarence Peters is a prominent Nigerian music video director and filmmaker known for his visually innovative work with many of Africa’s biggest music artists.
  • D. Seymour Wright
    Seymour Wright was an individual significant enough in regional history or exploration that Wright Peak in New York’s Adirondack Mountains was named in his honor.
  • E. Clarence F. Korstian
    Clarence F. Korstian was an American forester and educator known for his leadership in forest management and his role in developing forestry education and research in the United States.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d7bdf7c1f0819098102569a8d8cbf5 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9714581988190afc720ffd7797860 completed April 10, 2026, 9:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff7559f0448190a992f0770ac8227a completed May 9, 2026, 5:56 p.m.
Created at: April 9, 2026, 5:39 p.m.