Triple

T14033205
Position Surface form Disambiguated ID Type / Status
Subject Escola de Comando e Estado-Maior da Aeronáutica E337642 entity
Predicate abbreviation P43 FINISHED
Object ECEMAR
ECEMAR is the Brazilian Air Force’s Command and Staff School, responsible for advanced professional military education and leadership training for its officers.
 E1075023 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: ECEMAR | Statement: [Escola de Comando e Estado-Maior da Aeronáutica, abbreviation, ECEMAR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ECEMAR
Context triple: [Escola de Comando e Estado-Maior da Aeronáutica, abbreviation, ECEMAR]
  • A. CEC
    CEC is the primary state agency responsible for energy policy, planning, and regulation in California.
  • B. CEC
    CEC is the three-letter IATA airport code for Del Norte County Regional Airport in Crescent City, California.
  • C. CEC
    CEC is the commonly used abbreviation for the College of Engineering and Computing, an academic division focused on engineering and computing disciplines.
  • D. ECRA
    ECRA is a U.S. federal law that modernizes and strengthens controls on the export of sensitive technologies and goods important to national security and foreign policy.
  • E. RCEME
    RCEME is the corps of the Canadian Army responsible for the maintenance, repair, and recovery of military vehicles and equipment.
  • 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: ECEMAR
Triple: [Escola de Comando e Estado-Maior da Aeronáutica, abbreviation, ECEMAR]
Generated description
ECEMAR is the Brazilian Air Force’s Command and Staff School, responsible for advanced professional military education and leadership training for its officers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ECEMAR
Target entity description: ECEMAR is the Brazilian Air Force’s Command and Staff School, responsible for advanced professional military education and leadership training for its officers.
  • A. CEC
    CEC is the primary state agency responsible for energy policy, planning, and regulation in California.
  • B. CEC
    CEC is the three-letter IATA airport code for Del Norte County Regional Airport in Crescent City, California.
  • C. CEC
    CEC is the commonly used abbreviation for the College of Engineering and Computing, an academic division focused on engineering and computing disciplines.
  • D. ECRA
    ECRA is a U.S. federal law that modernizes and strengthens controls on the export of sensitive technologies and goods important to national security and foreign policy.
  • E. RCEME
    RCEME is the corps of the Canadian Army responsible for the maintenance, repair, and recovery of military vehicles and equipment.
  • 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_69d81c6543a48190bd5ba93d7419e797 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2fab17008190981f1808726fa11c completed April 14, 2026, 12:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbc337a5cc8190953b84255a401ada completed May 6, 2026, 10:39 p.m.
NEDg Description generation batch_69fbc558d980819080c64df19907b4ec completed May 6, 2026, 10:48 p.m.
NED2 Entity disambiguation (via description) batch_69fbc5d76cdc8190970778580437cf72 completed May 6, 2026, 10:51 p.m.
Created at: April 9, 2026, 10:20 p.m.