Triple

T506184
Position Surface form Disambiguated ID Type / Status
Subject Joe Gibbs E10506 entity
Predicate familyName P18 FINISHED
Object Gibbs
Gibbs is a common English surname borne by various notable individuals across fields such as sports, science, and entertainment.
 E62722 NE FINISHED

How this triple was built (4 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: Gibbs | Statement: [Joe Gibbs, familyName, Gibbs]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gibbs
Context triple: [Joe Gibbs, familyName, Gibbs]
  • A. Gage
    Gage is a surname of English origin borne by various notable individuals, including historical and contemporary figures.
  • B. Gilbert
    Gilbert is a rapidly growing suburban town in the southeastern Phoenix metropolitan area known for its family-friendly communities and high quality of life.
  • C. Gordon
    Gordon is the middle name of the famed Romantic poet Lord Byron, whose full name is George Gordon Byron.
  • D. Gassel
    Gassel is a village in the Dutch province of North Brabant, known historically as a separate municipality before being incorporated into a larger administrative unit.
  • E. Ficker
    Ficker is the birth surname of renowned American ballerina Suzanne Farrell, one of the most celebrated muses of choreographer George Balanchine.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Gibbs
Triple: [Joe Gibbs, familyName, Gibbs]
Generated description
Gibbs is a common English surname borne by various notable individuals across fields such as sports, science, and entertainment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gibbs
Target entity description: Gibbs is a common English surname borne by various notable individuals across fields such as sports, science, and entertainment.
  • A. Gage
    Gage is a surname of English origin borne by various notable individuals, including historical and contemporary figures.
  • B. Gilbert
    Gilbert is a rapidly growing suburban town in the southeastern Phoenix metropolitan area known for its family-friendly communities and high quality of life.
  • C. Gordon
    Gordon is the middle name of the famed Romantic poet Lord Byron, whose full name is George Gordon Byron.
  • D. Gassel
    Gassel is a village in the Dutch province of North Brabant, known historically as a separate municipality before being incorporated into a larger administrative unit.
  • E. Ficker
    Ficker is the birth surname of renowned American ballerina Suzanne Farrell, one of the most celebrated muses of choreographer George Balanchine.
  • F. None of above. chosen

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_69a2e848adf881908e5e04f7af030093 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f14c83f08190b1028f4929866db4 completed Feb. 28, 2026, 1:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69a48a7d47cc8190be1741f95f967f25 completed March 1, 2026, 6:50 p.m.
NEDg Description generation batch_69a48ae58a288190b4fa3e7a052477ca completed March 1, 2026, 6:52 p.m.
NED2 Entity disambiguation (via description) batch_69a48b35abe881909029c02557da0819 completed March 1, 2026, 6:53 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.