Triple

T5341009
Position Surface form Disambiguated ID Type / Status
Subject Federal Medical Assistance Percentage E123943 entity
Predicate hasAcronym P43 FINISHED
Object FMAP
FMAP is the federal government’s share of Medicaid program costs, used to determine how much federal funding each state receives for eligible medical services.
 E512124 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: FMAP | Statement: [Federal Medical Assistance Percentage, hasAcronym, FMAP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FMAP
Context triple: [Federal Medical Assistance Percentage, hasAcronym, FMAP]
  • A. FPM
    FPM is the Fellow Programme in Management, a doctoral-level research program in management studies offered by the Indian Institutes of Management.
  • B. FAM
    FAM is the acronym commonly used to refer to the Mexican Air Force, the aerial warfare branch of Mexico’s armed forces.
  • C. FMF
    FMF is the commonly used abbreviation for the Mexican Football Federation, the governing body of professional and amateur soccer in Mexico.
  • D. FAMO
    FAMO was a German vehicle manufacturer best known for producing military half-tracks and armored vehicles for the Wehrmacht during World War II.
  • E. SFM
    SFM is the station code for San Francisco's 4th and King Street Caltrain terminal, a major commuter rail hub in the city.
  • 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: FMAP
Triple: [Federal Medical Assistance Percentage, hasAcronym, FMAP]
Generated description
FMAP is the federal government’s share of Medicaid program costs, used to determine how much federal funding each state receives for eligible medical services.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FMAP
Target entity description: FMAP is the federal government’s share of Medicaid program costs, used to determine how much federal funding each state receives for eligible medical services.
  • A. FPM
    FPM is the Fellow Programme in Management, a doctoral-level research program in management studies offered by the Indian Institutes of Management.
  • B. FAM
    FAM is the acronym commonly used to refer to the Mexican Air Force, the aerial warfare branch of Mexico’s armed forces.
  • C. FMF
    FMF is the commonly used abbreviation for the Mexican Football Federation, the governing body of professional and amateur soccer in Mexico.
  • D. FAMO
    FAMO was a German vehicle manufacturer best known for producing military half-tracks and armored vehicles for the Wehrmacht during World War II.
  • E. SFM
    SFM is the station code for San Francisco's 4th and King Street Caltrain terminal, a major commuter rail hub in the city.
  • 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_69bd464b07f8819095aa76577c9829e4 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd85cb250c81908a48e4e2bbebbdb9 completed March 20, 2026, 5:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf18c8db388190a31f55854e7370fc completed March 21, 2026, 10:16 p.m.
NEDg Description generation batch_69bf19c8273081908a5138e9af921ec7 completed March 21, 2026, 10:20 p.m.
NED2 Entity disambiguation (via description) batch_69bf1a3049648190b5040e587671610a completed March 21, 2026, 10:22 p.m.
Created at: March 20, 2026, 2 p.m.