Triple

T13056493
Position Surface form Disambiguated ID Type / Status
Subject Hubert Laws E327588 entity
Predicate familyName P18 FINISHED
Object Laws
Laws is a common English surname borne by various notable individuals across fields such as music, sports, and public service.
 E549738 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: Laws | Statement: [Hubert Laws, familyName, Laws]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Laws
Context triple: [Hubert Laws, familyName, Laws]
  • A. Laws
    Laws is one of Plato’s late philosophical dialogues, presenting a detailed exploration of legal theory, political organization, and the ideal constitution for a well-ordered city.
  • B. Ley
    Ley is an alternative spelling of the given name Leigh, used as a personal name or surname in English-speaking contexts.
  • C. LAW
    LAW is the IATA airport code for Lawton–Fort Sill Regional Airport in Lawton, Oklahoma, United States.
  • D. The Law
    The Law is a 1974 American television film, written by Joel Oliansky, that offers a dramatic, behind-the-scenes look at the workings of the criminal justice system.
  • E. Law
    Law is a common English-language surname borne by various notable individuals, including political figures such as former British Prime Minister Andrew Bonar Law.
  • 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: Laws
Triple: [Hubert Laws, familyName, Laws]
Generated description
Laws is a common English surname borne by various notable individuals across fields such as music, sports, and public service.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Laws
Target entity description: Laws is a common English surname borne by various notable individuals across fields such as music, sports, and public service.
  • A. Laws
    Laws is one of Plato’s late philosophical dialogues, presenting a detailed exploration of legal theory, political organization, and the ideal constitution for a well-ordered city.
  • B. Ley
    Ley is an alternative spelling of the given name Leigh, used as a personal name or surname in English-speaking contexts.
  • C. LAW
    LAW is the IATA airport code for Lawton–Fort Sill Regional Airport in Lawton, Oklahoma, United States.
  • D. The Law
    The Law is a 1974 American television film, written by Joel Oliansky, that offers a dramatic, behind-the-scenes look at the workings of the criminal justice system.
  • E. Law chosen
    Law is a common English-language surname borne by various notable individuals, including political figures such as former British Prime Minister Andrew Bonar Law.
  • 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_69d8076e64308190904fb5c93517c901 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d980bd305c8190bcf191b2d35ec8de completed April 10, 2026, 10:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbdead348190aa7aaa29c371d72a completed May 3, 2026, 4:15 a.m.
NEDg Description generation batch_69f6d039254881909927b58225f194de completed May 3, 2026, 4:34 a.m.
NED2 Entity disambiguation (via description) batch_69f6d0d218d4819080273a151a0890d3 completed May 3, 2026, 4:36 a.m.
Created at: April 9, 2026, 8:58 p.m.