Triple

T37810066
Position Surface form Disambiguated ID Type / Status
Subject Against the Law E942615 entity
Predicate hasLegalSetting P88013 FINISHED
Object courtroom LITERAL FINISHED

How this triple was built (2 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: courtroom | Statement: [Against the Law, hasLegalSetting, courtroom]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasLegalSetting
Context triple: [Against the Law, hasLegalSetting, courtroom]
  • A. hasSetting
    Indicates that an entity takes place, occurs, or exists within a particular environment, context, or location.
  • B. legalSetting chosen
    Indicates that an entity is involved in, occurs within, or is characterized by a formal legal context, such as courts, legal procedures, or judicial environments.
  • C. hasLegalRight
    Indicates that an entity possesses an officially recognized legal entitlement or permission to perform an action or hold a claim regarding another entity.
  • D. hasLegalFunction
    Indicates that an entity performs, fulfills, or is assigned a specific legal role, duty, or function within a legal or regulatory context.
  • E. hasSettingBy
    Indicates that something (such as a work, event, or scenario) has its contextual environment, location, or background defined or established by a particular agent or source.
  • F. None of above.

Provenance (3 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_69f76ee8104c8190ab17133ccd8f86e6 completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fe9dfaa2d08190b2084f63f842eb6b completed May 9, 2026, 2:37 a.m.
PD Predicate disambiguation batch_69fe9bba947c81908b0b2b92a4d19b37 completed May 9, 2026, 2:28 a.m.
Created at: May 3, 2026, 4:19 p.m.