Triple

T4488837
Position Surface form Disambiguated ID Type / Status
Subject Allen, Texas E107317 entity
Predicate mayor P185 FINISHED
Object Bailey Moore
Bailey Moore is a local political leader serving as the mayor of Allen, Texas.
 E465862 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: Bailey Moore | Statement: [Allen, Texas, mayor, Bailey Moore]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bailey Moore
Context triple: [Allen, Texas, mayor, Bailey Moore]
  • A. Bailey Olter
    Bailey Olter was a Micronesian politician who served as the second President of the Federated States of Micronesia in the early 1990s.
  • B. Mallory Pugh
    Mallory Pugh is an American professional soccer player and U.S. women’s national team forward known for her speed, creativity, and impact at both club and international levels.
  • C. Cydney Daly
    Cydney Daly is known as the daughter of Hall of Fame NBA coach Chuck Daly.
  • D. Hadley Beeman
    Hadley Beeman is a web standards and technology governance expert known for her leadership within the World Wide Web Consortium (W3C) and related digital policy initiatives.
  • E. Lacey Pemberton
    Lacey Pemberton is a popular high school girl and one of the central characters in John Green’s novel and film adaptation "Paper Towns."
  • 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: Bailey Moore
Triple: [Allen, Texas, mayor, Bailey Moore]
Generated description
Bailey Moore is a local political leader serving as the mayor of Allen, Texas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bailey Moore
Target entity description: Bailey Moore is a local political leader serving as the mayor of Allen, Texas.
  • A. Bailey Olter
    Bailey Olter was a Micronesian politician who served as the second President of the Federated States of Micronesia in the early 1990s.
  • B. Mallory Pugh
    Mallory Pugh is an American professional soccer player and U.S. women’s national team forward known for her speed, creativity, and impact at both club and international levels.
  • C. Cydney Daly
    Cydney Daly is known as the daughter of Hall of Fame NBA coach Chuck Daly.
  • D. Hadley Beeman
    Hadley Beeman is a web standards and technology governance expert known for her leadership within the World Wide Web Consortium (W3C) and related digital policy initiatives.
  • E. Lacey Pemberton
    Lacey Pemberton is a popular high school girl and one of the central characters in John Green’s novel and film adaptation "Paper Towns."
  • 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_69bd43f84f788190a1383579c4a595be completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd52ad36748190b791de458f2116b2 completed March 20, 2026, 1:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69be396a2ff481908974870ceb903188 completed March 21, 2026, 6:23 a.m.
NEDg Description generation batch_69be3b9f151481909a05238656659340 completed March 21, 2026, 6:33 a.m.
NED2 Entity disambiguation (via description) batch_69be3c059b1c819084fa4c30e576fd2e completed March 21, 2026, 6:34 a.m.
Created at: March 20, 2026, 12:59 p.m.