Triple

T15159510
Position Surface form Disambiguated ID Type / Status
Subject Gray County E362169 entity
Predicate namedFor P63 FINISHED
Object Peter W. Gray
Peter W. Gray was a 19th-century Texas lawyer, judge, and politician who played a significant role in the state's early legal and political development.
 E1140751 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: Peter W. Gray | Statement: [Gray County, namedFor, Peter W. Gray]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Peter W. Gray
Context triple: [Gray County, namedFor, Peter W. Gray]
  • A. Peter Gray
    Peter Gray is the son of Maxine Gray, a central character in the television series "Judging Amy."
  • B. Alan Pariser
    Alan Pariser is a music industry figure best known as a co-founder and collaborator of prominent record producer and manager Lou Adler.
  • C. Richard Selley
    Richard Selley is a British geologist and petroleum sedimentologist known for his influential work on sedimentary basins and hydrocarbon exploration.
  • D. Barry K. Schwartz
    Barry K. Schwartz is an American businessman and co-founder of the Calvin Klein fashion brand.
  • E. Bruce D. Lucas
    Bruce D. Lucas is a computer scientist best known for co-developing the Lucas–Kanade method, a foundational algorithm in computer vision for estimating optical flow and image alignment.
  • 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: Peter W. Gray
Triple: [Gray County, namedFor, Peter W. Gray]
Generated description
Peter W. Gray was a 19th-century Texas lawyer, judge, and politician who played a significant role in the state's early legal and political development.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Peter W. Gray
Target entity description: Peter W. Gray was a 19th-century Texas lawyer, judge, and politician who played a significant role in the state's early legal and political development.
  • A. Peter Gray
    Peter Gray is the son of Maxine Gray, a central character in the television series "Judging Amy."
  • B. Alan Pariser
    Alan Pariser is a music industry figure best known as a co-founder and collaborator of prominent record producer and manager Lou Adler.
  • C. Richard Selley
    Richard Selley is a British geologist and petroleum sedimentologist known for his influential work on sedimentary basins and hydrocarbon exploration.
  • D. Barry K. Schwartz
    Barry K. Schwartz is an American businessman and co-founder of the Calvin Klein fashion brand.
  • E. Bruce D. Lucas
    Bruce D. Lucas is a computer scientist best known for co-developing the Lucas–Kanade method, a foundational algorithm in computer vision for estimating optical flow and image alignment.
  • 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_69d85a087b7c81908baa94a53dac8d68 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0060dd71881908ecc4a4f52d438a5 completed April 15, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69febffaa4b88190aab36fdae057e6d2 completed May 9, 2026, 5:02 a.m.
NEDg Description generation batch_69fec23c83ac819087633c0f64e50506 completed May 9, 2026, 5:12 a.m.
NED2 Entity disambiguation (via description) batch_69fec2f95a408190850e4d81839822ba completed May 9, 2026, 5:15 a.m.
Created at: April 10, 2026, 3:08 a.m.