Triple

T1314280
Position Surface form Disambiguated ID Type / Status
Subject Maritime and Coastguard Agency E28064 entity
Predicate abbreviation P43 FINISHED
Object MCA
MCA is the UK government executive agency responsible for maritime safety, search and rescue coordination, and preventing pollution from ships in UK waters.
 E150245 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: MCA | Statement: [Maritime and Coastguard Agency, abbreviation, MCA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MCA
Context triple: [Maritime and Coastguard Agency, abbreviation, MCA]
  • A. MCA
    MCA was a major American record label and entertainment company known for signing prominent artists and producing a wide range of popular music releases.
  • B. MCI
    MCI is a major commercial airport serving the Kansas City metropolitan area in Missouri, United States.
  • C. MGA
    MGA is the commonly used abbreviation for the Maryland General Assembly, the state’s bicameral legislative body.
  • D. MVD
    The MVD was the Soviet Ministry of Internal Affairs, a powerful state security and police organ that, among other functions, administered the Gulag forced labor camp system.
  • E. MAB
    MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
  • 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: MCA
Triple: [Maritime and Coastguard Agency, abbreviation, MCA]
Generated description
MCA is the UK government executive agency responsible for maritime safety, search and rescue coordination, and preventing pollution from ships in UK waters.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MCA
Target entity description: MCA is the UK government executive agency responsible for maritime safety, search and rescue coordination, and preventing pollution from ships in UK waters.
  • A. MCA
    MCA was a major American record label and entertainment company known for signing prominent artists and producing a wide range of popular music releases.
  • B. MCI
    MCI is a major commercial airport serving the Kansas City metropolitan area in Missouri, United States.
  • C. MGA
    MGA is the commonly used abbreviation for the Maryland General Assembly, the state’s bicameral legislative body.
  • D. MVD
    The MVD was the Soviet Ministry of Internal Affairs, a powerful state security and police organ that, among other functions, administered the Gulag forced labor camp system.
  • E. MAB
    MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
  • 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_69a498532c3481909223b74af2e578df completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c1598f488190b01cf4c3f1e613a7 completed March 1, 2026, 10:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbaf16a84819089c3473113ae70f9 completed March 7, 2026, 11:55 p.m.
NEDg Description generation batch_69acbb89269081909f00aee480c94e9f completed March 7, 2026, 11:58 p.m.
NED2 Entity disambiguation (via description) batch_69acbc010cd0819080b1f8695dc0990b completed March 8, 2026, midnight
Created at: March 1, 2026, 7:55 p.m.