Triple

T11897563
Position Surface form Disambiguated ID Type / Status
Subject U.S. Foreign Military Financing E283073 entity
Predicate alsoKnownAs P39 FINISHED
Object FMF
FMF is a U.S. government program that provides grants and loans to help foreign countries purchase American defense equipment, services, and training.
 E952400 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: FMF | Statement: [U.S. Foreign Military Financing, alsoKnownAs, FMF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FMF
Context triple: [U.S. Foreign Military Financing, alsoKnownAs, FMF]
  • A. FMF
    FMF is the commonly used abbreviation for the Mexican Football Federation, the governing body of professional and amateur soccer in Mexico.
  • B. FM!
    FM! is a 2018 studio album by American rapper Vince Staples that blends West Coast hip-hop with a conceptual radio-show format and sharp social commentary.
  • C. FFM
    FFM is an abbreviation commonly used for the Montreal World Film Festival, an international film festival held annually in Montreal, Canada.
  • D. FUM
    FUM is an abbreviation for Ferdowsi University of Mashhad, a major public research university in Mashhad, Iran.
  • E. MF
    MF is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas collectivity of Saint Martin.
  • 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: FMF
Triple: [U.S. Foreign Military Financing, alsoKnownAs, FMF]
Generated description
FMF is a U.S. government program that provides grants and loans to help foreign countries purchase American defense equipment, services, and training.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FMF
Target entity description: FMF is a U.S. government program that provides grants and loans to help foreign countries purchase American defense equipment, services, and training.
  • A. FMF
    FMF is the commonly used abbreviation for the Mexican Football Federation, the governing body of professional and amateur soccer in Mexico.
  • B. FM!
    FM! is a 2018 studio album by American rapper Vince Staples that blends West Coast hip-hop with a conceptual radio-show format and sharp social commentary.
  • C. FFM
    FFM is an abbreviation commonly used for the Montreal World Film Festival, an international film festival held annually in Montreal, Canada.
  • D. FUM
    FUM is an abbreviation for Ferdowsi University of Mashhad, a major public research university in Mashhad, Iran.
  • E. MF
    MF is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas collectivity of Saint Martin.
  • 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_69d6ab2a90b08190a4e818821cc93e6d completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8dd13cc10819089d8d5103e562924 completed April 10, 2026, 11:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69f418205b788190a4b1b81d89cf7fff completed May 1, 2026, 3:04 a.m.
NEDg Description generation batch_69f41f1c21388190b6ecb0fd602abb7d completed May 1, 2026, 3:33 a.m.
NED2 Entity disambiguation (via description) batch_69f42283c4cc81909793834ef65d2514 completed May 1, 2026, 3:48 a.m.
Created at: April 8, 2026, 9:44 p.m.