Triple

T9388318
Position Surface form Disambiguated ID Type / Status
Subject Naval Military School (Escuela Naval Militar) E225957 entity
Predicate hasAbbreviation P43 FINISHED
Object ENM
ENM is the commonly used abbreviation for the Naval Military School (Escuela Naval Militar), the institution responsible for training naval officers.
 E796106 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: ENM | Statement: [Naval Military School (Escuela Naval Militar), hasAbbreviation, ENM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ENM
Context triple: [Naval Military School (Escuela Naval Militar), hasAbbreviation, ENM]
  • A. ENA
    ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • B. Em
    Em is a common shortened form of the given name Emma, often used as an informal nickname.
  • C. ENE
    ENE was the stock ticker symbol for Enron Corporation, the American energy company infamous for its massive accounting fraud and subsequent 2001 bankruptcy.
  • D. ENH
    ENH is the IATA airport code for Enshi Xujiaping Airport, a regional airport serving Enshi in Hubei Province, China.
  • E. ENBR
    ENBR is the ICAO airport code for Bergen Airport, Flesland, the main international airport serving Bergen, Norway.
  • 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: ENM
Triple: [Naval Military School (Escuela Naval Militar), hasAbbreviation, ENM]
Generated description
ENM is the commonly used abbreviation for the Naval Military School (Escuela Naval Militar), the institution responsible for training naval officers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ENM
Target entity description: ENM is the commonly used abbreviation for the Naval Military School (Escuela Naval Militar), the institution responsible for training naval officers.
  • A. ENA
    ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • B. Em
    Em is a common shortened form of the given name Emma, often used as an informal nickname.
  • C. ENE
    ENE was the stock ticker symbol for Enron Corporation, the American energy company infamous for its massive accounting fraud and subsequent 2001 bankruptcy.
  • D. ENH
    ENH is the IATA airport code for Enshi Xujiaping Airport, a regional airport serving Enshi in Hubei Province, China.
  • E. ENBR
    ENBR is the ICAO airport code for Bergen Airport, Flesland, the main international airport serving Bergen, Norway.
  • 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_69ca842e9dcc8190a264119e683cfe04 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd50d6e5f081908102909cb4cbb649 completed April 1, 2026, 5:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69d100f1182881909c3b11c22e911b61 completed April 4, 2026, 12:15 p.m.
NEDg Description generation batch_69d1017a36e0819091bd6d7bc75d1a97 completed April 4, 2026, 12:18 p.m.
NED2 Entity disambiguation (via description) batch_69d10270f9948190bbf937089f88bacf completed April 4, 2026, 12:22 p.m.
Created at: March 30, 2026, 7:45 p.m.