Triple

T11610821
Position Surface form Disambiguated ID Type / Status
Subject Bureau of Fisheries and Aquatic Resources E275378 entity
Predicate abbreviation P43 FINISHED
Object BFAR
BFAR is a Philippine government agency responsible for the development, management, and conservation of the country’s fisheries and aquatic resources.
 E936593 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: BFAR | Statement: [Bureau of Fisheries and Aquatic Resources, abbreviation, BFAR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BFAR
Context triple: [Bureau of Fisheries and Aquatic Resources, abbreviation, BFAR]
  • A. BFP
    BFP is the three-letter IATA airport code assigned to Beaver County Airport in Pennsylvania, United States.
  • B. BAF
    BAF is the service number prefix used to identify personnel of the Belgian Air Component (Belgian Air Force).
  • C. FAR
    FAR is the acronym for Cuba’s national military organization, the Revolutionary Armed Forces.
  • D. FAR
    FAR is the commonly used abbreviation for the Intergovernmental Panel on Climate Change’s First Assessment Report, a foundational scientific evaluation of climate change published in 1990.
  • E. FAR
    FAR is the station code for Faro railway station, a key rail transport hub serving the city of Faro in southern Portugal’s Algarve region.
  • 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: BFAR
Triple: [Bureau of Fisheries and Aquatic Resources, abbreviation, BFAR]
Generated description
BFAR is a Philippine government agency responsible for the development, management, and conservation of the country’s fisheries and aquatic resources.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: BFAR
Target entity description: BFAR is a Philippine government agency responsible for the development, management, and conservation of the country’s fisheries and aquatic resources.
  • A. BFP
    BFP is the three-letter IATA airport code assigned to Beaver County Airport in Pennsylvania, United States.
  • B. BAF
    BAF is the service number prefix used to identify personnel of the Belgian Air Component (Belgian Air Force).
  • C. FAR
    FAR is the acronym for Cuba’s national military organization, the Revolutionary Armed Forces.
  • D. FAR
    FAR is the commonly used abbreviation for the Intergovernmental Panel on Climate Change’s First Assessment Report, a foundational scientific evaluation of climate change published in 1990.
  • E. FAR
    FAR is the station code for Faro railway station, a key rail transport hub serving the city of Faro in southern Portugal’s Algarve region.
  • 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_69d6aaf84b548190ac072e4fb89ae18f completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a043a3c08190a20cbc2ba5a8d218 completed April 10, 2026, 7:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69e8a8311bcc8190a3fe7d28c593aea3 completed April 22, 2026, 10:51 a.m.
NEDg Description generation batch_69e8af9665648190b7732076aa129671 completed April 22, 2026, 11:23 a.m.
NED2 Entity disambiguation (via description) batch_69ee5b3a3720819095a4a87176e052cb completed April 26, 2026, 6:36 p.m.
Created at: April 8, 2026, 9:38 p.m.