Triple

T15944314
Position Surface form Disambiguated ID Type / Status
Subject FN Herstal E386645 entity
Predicate shortName P43 FINISHED
Object FN
FN is a renowned Belgian firearms manufacturer known for producing military and civilian small arms used worldwide.
 E1185183 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: FN | Statement: [FN Herstal, shortName, FN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FN
Context triple: [FN Herstal, shortName, FN]
  • A. FN
    FN is the vehicle registration code used on license plates for vehicles registered in the German city of Friedrichshafen.
  • B. FNJ
    FNJ is the IATA airport code for Pyongyang Sunan International Airport, the main international gateway to North Korea’s capital.
  • C. FP
    FP is the station code for Floral Park station on the Long Island Rail Road in New York.
  • D. NF
    NF is a set theory introduced by W.V.O. Quine that modifies standard axioms to avoid certain paradoxes while allowing a universal set.
  • E. NF
    NF was the abbreviation for the National Front of the German Democratic Republic, a political alliance dominated by the ruling Socialist Unity Party that coordinated and controlled all legal political activity in East Germany.
  • 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: FN
Triple: [FN Herstal, shortName, FN]
Generated description
FN is a renowned Belgian firearms manufacturer known for producing military and civilian small arms used worldwide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FN
Target entity description: FN is a renowned Belgian firearms manufacturer known for producing military and civilian small arms used worldwide.
  • A. FN
    FN is the vehicle registration code used on license plates for vehicles registered in the German city of Friedrichshafen.
  • B. FNJ
    FNJ is the IATA airport code for Pyongyang Sunan International Airport, the main international gateway to North Korea’s capital.
  • C. FP
    FP is the station code for Floral Park station on the Long Island Rail Road in New York.
  • D. NF
    NF is a set theory introduced by W.V.O. Quine that modifies standard axioms to avoid certain paradoxes while allowing a universal set.
  • E. NF
    NF was the abbreviation for the National Front of the German Democratic Republic, a political alliance dominated by the ruling Socialist Unity Party that coordinated and controlled all legal political activity in East Germany.
  • 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_69d86da750008190987eb26be3f6c118 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e156d016588190ae368197dfa7d43a completed April 16, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb5beabbc8190977f14c1b3ccdf29 completed May 9, 2026, 10:31 p.m.
NEDg Description generation batch_69ffb677927c8190bd45e7bae5fcf1ed completed May 9, 2026, 10:34 p.m.
NED2 Entity disambiguation (via description) batch_69ffb7468fb88190a56cf1df5bd20f63 completed May 9, 2026, 10:37 p.m.
Created at: April 10, 2026, 4:53 a.m.