Triple

T20749658
Position Surface form Disambiguated ID Type / Status
Subject Food and Nutrition Information Center E510682 entity
Predicate hasAbbreviation P43 FINISHED
Object FNIC
FNIC is a U.S. government resource center that provides reliable, science-based information on food, nutrition, and diet to professionals and the public.
 E1449221 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: FNIC | Statement: [Food and Nutrition Information Center, hasAbbreviation, FNIC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FNIC
Context triple: [Food and Nutrition Information Center, hasAbbreviation, FNIC]
  • A. FNI
    FNI is the IATA airport code for Nîmes-Alès-Camargue-Cévennes Airport in southern France.
  • B. FNIA
    FNIA is the commonly used acronym for "Football Night in America," NBC's flagship Sunday night NFL pregame show and broadcast.
  • C. FNC
    FNC is the IATA airport code for Cristiano Ronaldo Madeira International Airport, the main air gateway to Portugal’s Madeira Island.
  • D. FNC
    FNC is the commonly used abbreviation for the United Arab Emirates’ Federal National Council, the country’s consultative parliamentary body.
  • E. FNWI
    FNWI is the Faculty of Science of the University of Amsterdam, encompassing a broad range of natural sciences, mathematics, and computer science education and research.
  • 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: FNIC
Triple: [Food and Nutrition Information Center, hasAbbreviation, FNIC]
Generated description
FNIC is a U.S. government resource center that provides reliable, science-based information on food, nutrition, and diet to professionals and the public.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FNIC
Target entity description: FNIC is a U.S. government resource center that provides reliable, science-based information on food, nutrition, and diet to professionals and the public.
  • A. FNI
    FNI is the IATA airport code for Nîmes-Alès-Camargue-Cévennes Airport in southern France.
  • B. FNIA
    FNIA is the commonly used acronym for "Football Night in America," NBC's flagship Sunday night NFL pregame show and broadcast.
  • C. FNC
    FNC is the IATA airport code for Cristiano Ronaldo Madeira International Airport, the main air gateway to Portugal’s Madeira Island.
  • D. FNC
    FNC is the commonly used abbreviation for the United Arab Emirates’ Federal National Council, the country’s consultative parliamentary body.
  • E. FNWI
    FNWI is the Faculty of Science of the University of Amsterdam, encompassing a broad range of natural sciences, mathematics, and computer science education and research.
  • 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_69e0b4c845e88190b4c5f3ae79291182 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c229913081908eadf716195071de completed April 21, 2026, 12:17 a.m.
NED1 Entity disambiguation (via context triple) batch_6a08e84489c481908fa6ecbd9bae07d1 completed May 16, 2026, 9:57 p.m.
NEDg Description generation batch_6a08e974acc08190b6d306e2d6385dd8 completed May 16, 2026, 10:02 p.m.
NED2 Entity disambiguation (via description) batch_6a08ead93acc81908f81b70cf9ce0683 completed May 16, 2026, 10:08 p.m.
Created at: April 16, 2026, 12:34 p.m.