Triple

T1353317
Position Surface form Disambiguated ID Type / Status
Subject École nationale d'administration E28931 entity
Predicate shortName P43 FINISHED
Object ENA
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
 E155153 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: ENA | Statement: [École nationale d'administration, shortName, ENA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ENA
Context triple: [École nationale d'administration, shortName, ENA]
  • A. ANA
    ANA is the standard three-letter abbreviation used for the Anaheim Ducks, a professional ice hockey team in the National Hockey League.
  • B. NE
    NE is the official two-letter United States Postal Service abbreviation used to designate the state of Nebraska.
  • C. NE
    NE is the Swiss vehicle registration code for the canton of Neuchâtel.
  • D. NE
    NE is the two-letter ISO 3166-1 alpha-2 country code assigned to Niger.
  • E. NE
    NE is the common abbreviation for the New England Revolution, a professional Major League Soccer club based in the Greater Boston area.
  • 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: ENA
Triple: [École nationale d'administration, shortName, ENA]
Generated description
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ENA
Target entity description: ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • A. ANA
    ANA is the standard three-letter abbreviation used for the Anaheim Ducks, a professional ice hockey team in the National Hockey League.
  • B. NE
    NE is the official two-letter United States Postal Service abbreviation used to designate the state of Nebraska.
  • C. NE
    NE is the Swiss vehicle registration code for the canton of Neuchâtel.
  • D. NE
    NE is the common abbreviation for the New England Revolution, a professional Major League Soccer club based in the Greater Boston area.
  • E. NE
    NE is the commonly used abbreviation for Natural England, the UK government’s adviser on the natural environment in England.
  • 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_69a498571d248190a0ac9eb02d97097f completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c26e916c8190b4b324df87f4c121 completed March 1, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69acce6a1dd48190b17ac5a7cf3ab933 completed March 8, 2026, 1:18 a.m.
NEDg Description generation batch_69acceac84748190bbf9dd9c9ba5561f completed March 8, 2026, 1:19 a.m.
NED2 Entity disambiguation (via description) batch_69accf1778b081908936977cd9317774 completed March 8, 2026, 1:21 a.m.
Created at: March 1, 2026, 7:56 p.m.