Triple

T1789110
Position Surface form Disambiguated ID Type / Status
Subject John Edward Gray E39454 entity
Predicate familyName P18 FINISHED
Object Gray
Gray is a common English surname borne by numerous notable figures across fields such as science, politics, literature, and the arts.
 E82493 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: Gray | Statement: [John Edward Gray, familyName, Gray]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gray
Context triple: [John Edward Gray, familyName, Gray]
  • A. Gray
    Gray is the commonly used short form of the name Gray Davis, the former governor of California.
  • B. Gray
    Gray is a historic commune in eastern France known for its picturesque setting along the Saône River and its well-preserved old town.
  • C. Brown
    Brown is a common English-language surname of Anglo-Saxon origin, typically derived from a nickname referring to hair color, complexion, or clothing.
  • D. Maroon
    Maroon refers to the descendants of escaped African slaves in the Americas who formed independent communities, notably in places like Suriname and Jamaica, preserving distinct African-derived cultures and traditions.
  • E. Blanc
    Blanc is the surname of Mel Blanc, the legendary American voice actor best known for bringing to life many iconic Looney Tunes characters.
  • 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: Gray
Triple: [John Edward Gray, familyName, Gray]
Generated description
Gray is a common English surname borne by numerous notable figures across fields such as science, politics, literature, and the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gray
Target entity description: Gray is a common English surname borne by numerous notable figures across fields such as science, politics, literature, and the arts.
  • A. Gray chosen
    Gray is the commonly used short form of the name Gray Davis, the former governor of California.
  • B. Gray
    Gray is a historic commune in eastern France known for its picturesque setting along the Saône River and its well-preserved old town.
  • C. Brown
    Brown is a common English-language surname of Anglo-Saxon origin, typically derived from a nickname referring to hair color, complexion, or clothing.
  • D. Maroon
    Maroon refers to the descendants of escaped African slaves in the Americas who formed independent communities, notably in places like Suriname and Jamaica, preserving distinct African-derived cultures and traditions.
  • E. Blanc
    Blanc is the surname of Mel Blanc, the legendary American voice actor best known for bringing to life many iconic Looney Tunes characters.
  • F. None of above.

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_69a88631854081909723959921e45c2b completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa65111e5481909c22abb6ad966814 completed March 6, 2026, 5:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada9a8a69c8190885bf06a06d3869f completed March 8, 2026, 4:54 p.m.
NEDg Description generation batch_69adaab488ec81909a340aab4916b90f completed March 8, 2026, 4:58 p.m.
NED2 Entity disambiguation (via description) batch_69adaf3cd23081909dd27c5de8e3f6d2 completed March 8, 2026, 5:17 p.m.
Created at: March 4, 2026, 7:32 p.m.