Triple

T10277457
Position Surface form Disambiguated ID Type / Status
Subject Center for Security Forces E241005 entity
Predicate shortName P43 FINISHED
Object CENSECFOR
CENSECFOR is a U.S. Navy training command that specializes in preparing personnel for security, force protection, and anti-terrorism missions.
 E851991 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: CENSECFOR | Statement: [Center for Security Forces, shortName, CENSECFOR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CENSECFOR
Context triple: [Center for Security Forces, shortName, CENSECFOR]
  • A. CFE
    CFE is the IATA airport code for Clermont-Ferrand Auvergne Airport in central France.
  • B. 1SECFOR
    1SECFOR is a United States Air Force security forces unit responsible for base defense, law enforcement, and force protection operations.
  • C. CESCE
    CESCE is a Spanish state-backed credit insurance and risk management company that has played a key role in the ownership and development of the technology and consulting firm Indra Sistemas.
  • D. CESE
    CESE is France’s Economic, Social and Environmental Council, a constitutional advisory body that represents civil society and provides expert opinions on public policy.
  • E. CONSER
    CONSER is a cooperative international program through which libraries collaboratively create, maintain, and share high-quality serials cataloging records.
  • 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: CENSECFOR
Triple: [Center for Security Forces, shortName, CENSECFOR]
Generated description
CENSECFOR is a U.S. Navy training command that specializes in preparing personnel for security, force protection, and anti-terrorism missions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CENSECFOR
Target entity description: CENSECFOR is a U.S. Navy training command that specializes in preparing personnel for security, force protection, and anti-terrorism missions.
  • A. CFE
    CFE is the IATA airport code for Clermont-Ferrand Auvergne Airport in central France.
  • B. 1SECFOR
    1SECFOR is a United States Air Force security forces unit responsible for base defense, law enforcement, and force protection operations.
  • C. CESCE
    CESCE is a Spanish state-backed credit insurance and risk management company that has played a key role in the ownership and development of the technology and consulting firm Indra Sistemas.
  • D. CESE
    CESE is France’s Economic, Social and Environmental Council, a constitutional advisory body that represents civil society and provides expert opinions on public policy.
  • E. CONSER
    CONSER is a cooperative international program through which libraries collaboratively create, maintain, and share high-quality serials cataloging records.
  • 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_69d381a94c1881908fc38fc263d9b9c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d28c3b10819093cdab1392384dd4 completed April 7, 2026, 9:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6f82188588190998e06cad1e15e68 completed April 9, 2026, 12:51 a.m.
NEDg Description generation batch_69d6fcad625881909304201c1ebb3bcb completed April 9, 2026, 1:11 a.m.
NED2 Entity disambiguation (via description) batch_69d6fd84cd708190816d94417294b52a completed April 9, 2026, 1:14 a.m.
Created at: April 6, 2026, 11:37 a.m.