Triple

T2900420
Position Surface form Disambiguated ID Type / Status
Subject Joint Staff of the French Armed Forces E62640 entity
Predicate shortName P43 FINISHED
Object EMA
EMA is the abbreviated name for the Joint Staff headquarters that oversees and coordinates the operations of the French Armed Forces.
 E308397 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: EMA | Statement: [Joint Staff of the French Armed Forces, shortName, EMA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: EMA
Context triple: [Joint Staff of the French Armed Forces, shortName, EMA]
  • A. EMA
    EMA is the European Union’s regulatory authority responsible for the scientific evaluation, supervision, and safety monitoring of medicines.
  • B. EMA
    EMA is the three-letter IATA airport code for East Midlands Airport in England, which serves the East Midlands region with domestic and international flights.
  • C. Em
    Em is a common shortened form of the given name Emma, often used as an informal nickname.
  • D. ERM
    ERM is the French-language abbreviation for Belgium’s Royal Military Academy, the country’s principal institution for training future officers of the armed forces.
  • E. ERM
    ERM is a European Union system designed to reduce exchange rate variability and achieve monetary stability in preparation for economic and monetary union.
  • 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: EMA
Triple: [Joint Staff of the French Armed Forces, shortName, EMA]
Generated description
EMA is the abbreviated name for the Joint Staff headquarters that oversees and coordinates the operations of the French Armed Forces.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: EMA
Target entity description: EMA is the abbreviated name for the Joint Staff headquarters that oversees and coordinates the operations of the French Armed Forces.
  • A. EMA
    EMA is the European Union’s regulatory authority responsible for the scientific evaluation, supervision, and safety monitoring of medicines.
  • B. EMA
    EMA is the three-letter IATA airport code for East Midlands Airport in England, which serves the East Midlands region with domestic and international flights.
  • C. Em
    Em is a common shortened form of the given name Emma, often used as an informal nickname.
  • D. ERM
    ERM is the French-language abbreviation for Belgium’s Royal Military Academy, the country’s principal institution for training future officers of the armed forces.
  • E. ERM
    ERM is a European Union system designed to reduce exchange rate variability and achieve monetary stability in preparation for economic and monetary union.
  • 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_69ab4c3e070c8190b78d3d2c005876dd completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abe0b081308190af8875151fb11c4e completed March 7, 2026, 8:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0318f2c908190aa10aa93f8fb139c completed March 10, 2026, 2:58 p.m.
NEDg Description generation batch_69b0405be9008190b1a264bc20e794c6 completed March 10, 2026, 4:01 p.m.
NED2 Entity disambiguation (via description) batch_69b044ad7cd48190b4a3ae143131616e completed March 10, 2026, 4:19 p.m.
Created at: March 6, 2026, 10:10 p.m.