Triple

T22991273
Position Surface form Disambiguated ID Type / Status
Subject Federal Police of Brazil E572054 entity
Predicate abbreviation P43 FINISHED
Object PF
PF is the Federal Police of Brazil, the national law enforcement agency responsible for investigating federal crimes, border control, and immigration.
 E1564408 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: PF | Statement: [Federal Police of Brazil, abbreviation, PF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PF
Context triple: [Federal Police of Brazil, abbreviation, PF]
  • A. PF
    PF is the abbreviation for the Mexican Federal Police, the former national law enforcement agency responsible for federal policing and public security in Mexico.
  • B. PF
    PF is the vehicle registration code used on license plates for the German city of Pforzheim.
  • C. FP
    FP is the Euronext Paris stock ticker symbol for Total S.A., the French multinational integrated oil and gas company.
  • D. FP
    FP is the station code for Fehrbelliner Platz, a public transit station in Berlin, Germany.
  • E. FP
    FP is the station code for Floral Park station on the Long Island Rail Road in New York.
  • 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: PF
Triple: [Federal Police of Brazil, abbreviation, PF]
Generated description
PF is the Federal Police of Brazil, the national law enforcement agency responsible for investigating federal crimes, border control, and immigration.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PF
Target entity description: PF is the Federal Police of Brazil, the national law enforcement agency responsible for investigating federal crimes, border control, and immigration.
  • A. PF
    PF is the vehicle registration code used on license plates for the German city of Pforzheim.
  • B. PF
    PF is the abbreviation for the Mexican Federal Police, the former national law enforcement agency responsible for federal policing and public security in Mexico.
  • C. FP
    FP is the station code for Fehrbelliner Platz, a public transit station in Berlin, Germany.
  • D. FP
    FP is the Euronext Paris stock ticker symbol for Total S.A., the French multinational integrated oil and gas company.
  • E. FP
    FP is the station code for Floral Park station on the Long Island Rail Road in New York.
  • 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_69e245b535808190adef8a9df3c584db completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f182eefd688190853977421540b2ce completed April 29, 2026, 4:02 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0bd37b1a0c819086bf665e96c2a540 completed May 19, 2026, 3:05 a.m.
NEDg Description generation batch_6a0bd44a4434819093871158e49bafbc completed May 19, 2026, 3:08 a.m.
NED2 Entity disambiguation (via description) batch_6a0bd4e53024819084592e995cc902e7 completed May 19, 2026, 3:11 a.m.
Created at: April 17, 2026, 3:50 p.m.