Triple

T14753458
Position Surface form Disambiguated ID Type / Status
Subject Fast Interaction Trigger detector E346671 entity
Predicate hasAcronym P43 FINISHED
Object FIT
FIT is a specialized Fast Interaction Trigger detector used in high-energy physics experiments to rapidly identify and record particle collision events.
 E1117452 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: FIT | Statement: [Fast Interaction Trigger detector, hasAcronym, FIT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FIT
Context triple: [Fast Interaction Trigger detector, hasAcronym, FIT]
  • A. FIT
    FIT is a software testing framework designed to facilitate collaboration between developers and customers by expressing and automatically checking requirements in tabular form.
  • B. FIT
    FIT is the National Rail station code for Filton Abbey Wood railway station in Bristol, England.
  • C. FIT
    FIT is a private research university in Melbourne, Florida, known for its strong programs in engineering, science, and aeronautics.
  • D. FIT
    FIT is a renowned New York City-based college specializing in fashion, design, art, business, and technology, and is part of the State University of New York (SUNY) system.
  • E. /fit/
    /fit/ is 4chan’s fitness board, dedicated to discussions about exercise, bodybuilding, weight loss, nutrition, and general physical self-improvement.
  • 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: FIT
Triple: [Fast Interaction Trigger detector, hasAcronym, FIT]
Generated description
FIT is a specialized Fast Interaction Trigger detector used in high-energy physics experiments to rapidly identify and record particle collision events.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FIT
Target entity description: FIT is a specialized Fast Interaction Trigger detector used in high-energy physics experiments to rapidly identify and record particle collision events.
  • A. FIT
    FIT is a software testing framework designed to facilitate collaboration between developers and customers by expressing and automatically checking requirements in tabular form.
  • B. FIT
    FIT is the National Rail station code for Filton Abbey Wood railway station in Bristol, England.
  • C. FIT
    FIT is a private research university in Melbourne, Florida, known for its strong programs in engineering, science, and aeronautics.
  • D. FIT
    FIT is a renowned New York City-based college specializing in fashion, design, art, business, and technology, and is part of the State University of New York (SUNY) system.
  • E. /fit/
    /fit/ is 4chan’s fitness board, dedicated to discussions about exercise, bodybuilding, weight loss, nutrition, and general physical self-improvement.
  • 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_69d822e8896c819091169882f9b20486 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec7d59df08190a86da5048358bd6e completed April 14, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdfb9c725881908004368772fa528d completed May 8, 2026, 3:05 p.m.
NEDg Description generation batch_69fdfde8ed30819083600cdac241675e completed May 8, 2026, 3:14 p.m.
NED2 Entity disambiguation (via description) batch_69fdfe969a6081908f4ebec0f6538811 completed May 8, 2026, 3:17 p.m.
Created at: April 10, 2026, 1:30 a.m.