Triple

T2645376
Position Surface form Disambiguated ID Type / Status
Subject MC Alger E62970 entity
Predicate shortName P43 FINISHED
Object MCA
MCA is a prominent Algerian football club based in Algiers, officially known as Mouloudia Club d'Alger.
 E285470 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: [MC Alger, shortName, MCA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MCA
Context triple: [MC Alger, shortName, MCA]
  • A. MCA
    MCA is the UK government executive agency responsible for maritime safety, search and rescue coordination, and preventing pollution from ships in UK waters.
  • B. 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.
  • C. MCC
    MCC is the abbreviated name of Belgium’s naval branch within the Belgian Armed Forces.
  • D. MCC
    MCC is a U.S. foreign aid agency that provides time-limited grants to promote economic growth, reduce poverty, and strengthen institutions in developing countries.
  • E. MCD
    MCD is a system of urban and suburban commuter rail lines in Moscow designed to function like an express metro, connecting the city with its surrounding regions.
  • 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: [MC Alger, shortName, MCA]
Generated description
MCA is a prominent Algerian football club based in Algiers, officially known as Mouloudia Club d'Alger.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MCA
Target entity description: MCA is a prominent Algerian football club based in Algiers, officially known as Mouloudia Club d'Alger.
  • 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. MCA
    MCA is the UK government executive agency responsible for maritime safety, search and rescue coordination, and preventing pollution from ships in UK waters.
  • C. MCC
    MCC is the abbreviated name of Belgium’s naval branch within the Belgian Armed Forces.
  • D. MCC
    MCC is a U.S. foreign aid agency that provides time-limited grants to promote economic growth, reduce poverty, and strengthen institutions in developing countries.
  • E. MCD
    MCD is a system of urban and suburban commuter rail lines in Moscow designed to function like an express metro, connecting the city with its surrounding regions.
  • 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_69ab4c3f2dcc819082df80f5e032f690 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abd917192081908e7a2cf780a17b83 completed March 7, 2026, 7:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69af98c50d108190b716dd51c34d3759 completed March 10, 2026, 4:06 a.m.
NEDg Description generation batch_69af9952a95881908d01aa13f5feef43 completed March 10, 2026, 4:08 a.m.
NED2 Entity disambiguation (via description) batch_69af99e68f10819094d758d3a4bc2e9c completed March 10, 2026, 4:11 a.m.
Created at: March 6, 2026, 9:53 p.m.