Triple

T11567597
Position Surface form Disambiguated ID Type / Status
Subject Maury Laws E274295 entity
Predicate familyName P18 FINISHED
Object Laws
Laws is a common English surname of Anglo-Norman origin, often derived as a patronymic form of "Law" or "Lawrence."
 E36435 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: [Maury Laws, familyName, Laws]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Laws
Context triple: [Maury 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. 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.
  • D. Law
    Law is the system of rules and principles recognized by a community or government as regulating the actions of its members and enforceable by legal institutions.
  • 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: [Maury Laws, familyName, Laws]
Generated description
Laws is a common English surname of Anglo-Norman origin, often derived as a patronymic form of "Law" or "Lawrence."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Laws
Target entity description: Laws is a common English surname of Anglo-Norman origin, often derived as a patronymic form of "Law" or "Lawrence."
  • A. Laws chosen
    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. 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.
  • D. 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.
  • E. Law
    Law is the system of rules and principles recognized by a community or government as regulating the actions of its members and enforceable by legal institutions.
  • 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_69d6aae5ac3c81908d2b0a3a665665b2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d88dd4305c8190ac5ff490b6b63e12 completed April 10, 2026, 5:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69e6e8c9a22c8190812c64b9f305ae99 completed April 21, 2026, 3:02 a.m.
NEDg Description generation batch_69e6ef9631e48190aef47bba9ad611e8 completed April 21, 2026, 3:31 a.m.
NED2 Entity disambiguation (via description) batch_69e6f94ac2d0819098a3024eaab908b5 completed April 21, 2026, 4:12 a.m.
Created at: April 8, 2026, 9:37 p.m.