Triple

T1050602
Position Surface form Disambiguated ID Type / Status
Subject Teaching Regulation Agency E22688 entity
Predicate abbreviation P43 FINISHED
Object TRA
TRA is the UK government body responsible for regulating the teaching profession, including overseeing teacher misconduct and maintaining professional standards.
 E120829 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: TRA | Statement: [Teaching Regulation Agency, abbreviation, TRA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TRA
Context triple: [Teaching Regulation Agency, abbreviation, TRA]
  • A. TR
    TR is the two-letter ISO 3166-1 alpha-2 country code assigned to Turkey for international standardization and referencing.
  • B. TOR
    TOR is the standard three-letter abbreviation used to represent the Toronto Maple Leafs in sports standings, statistics, and media.
  • C. TW
    TW is the two-letter ISO 3166 country code assigned to Taiwan (commonly referred to as Chinese Taipei in certain international contexts).
  • D. TRPA
    TRPA is a bi-state regional planning agency responsible for environmental protection and land-use regulation in the Lake Tahoe Basin.
  • E. TU
    TU is the international vehicle registration code assigned to Tunisia.
  • 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: TRA
Triple: [Teaching Regulation Agency, abbreviation, TRA]
Generated description
TRA is the UK government body responsible for regulating the teaching profession, including overseeing teacher misconduct and maintaining professional standards.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TRA
Target entity description: TRA is the UK government body responsible for regulating the teaching profession, including overseeing teacher misconduct and maintaining professional standards.
  • A. TR
    TR is the two-letter ISO 3166-1 alpha-2 country code assigned to Turkey for international standardization and referencing.
  • B. TOR
    TOR is the standard three-letter abbreviation used to represent the Toronto Maple Leafs in sports standings, statistics, and media.
  • C. TW
    TW is the two-letter ISO 3166 country code assigned to Taiwan (commonly referred to as Chinese Taipei in certain international contexts).
  • D. TRPA
    TRPA is a bi-state regional planning agency responsible for environmental protection and land-use regulation in the Lake Tahoe Basin.
  • E. TU
    TU is the international vehicle registration code assigned to Tunisia.
  • 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_69a493da02e081908c13ff5e02a0fe7a completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b8b40d50819091cb37a2236e82ee completed March 1, 2026, 10:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac3bcd3a4481908f7d9f13e3697fa9 completed March 7, 2026, 2:53 p.m.
NEDg Description generation batch_69ac3c43baec819086ab5fce7f5b1137 completed March 7, 2026, 2:54 p.m.
NED2 Entity disambiguation (via description) batch_69ac3c9cc1ec819087df1f4c4efc5646 completed March 7, 2026, 2:56 p.m.
Created at: March 1, 2026, 7:42 p.m.