Triple

T22460347
Position Surface form Disambiguated ID Type / Status
Subject Total S.A. E555211 entity
Predicate tickerSymbol P1447 FINISHED
Object FP
FP is the Euronext Paris stock ticker symbol for Total S.A., the French multinational integrated oil and gas company.
 E1538151 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: FP | Statement: [Total S.A., tickerSymbol, FP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FP
Context triple: [Total S.A., tickerSymbol, FP]
  • A. FP
    FP is the station code for Floral Park station on the Long Island Rail Road in New York.
  • B. FP
    FP is the station code for Fehrbelliner Platz, a public transit station in Berlin, Germany.
  • C. PF
    PF is the vehicle registration code used on license plates for the German city of Pforzheim.
  • D. FPF
    FPF is the Peruvian Football Federation, the main organization responsible for overseeing and regulating football activities in Peru.
  • E. FPF
    FPF is the Portuguese Football Federation, the national governing body responsible for organizing and overseeing football in Portugal, including its national teams.
  • 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: FP
Triple: [Total S.A., tickerSymbol, FP]
Generated description
FP is the Euronext Paris stock ticker symbol for Total S.A., the French multinational integrated oil and gas company.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FP
Target entity description: FP is the Euronext Paris stock ticker symbol for Total S.A., the French multinational integrated oil and gas company.
  • A. FP
    FP is the station code for Floral Park station on the Long Island Rail Road in New York.
  • B. FP
    FP is the station code for Fehrbelliner Platz, a public transit station in Berlin, Germany.
  • C. PF
    PF is the vehicle registration code used on license plates for the German city of Pforzheim.
  • D. FPF
    FPF is the Peruvian Football Federation, the main organization responsible for overseeing and regulating football activities in Peru.
  • E. FPF
    FPF is the Portuguese Football Federation, the national governing body responsible for organizing and overseeing football in Portugal, including its national teams.
  • 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_69e11e51fdec8190adfdf9f8a6362221 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15b7eb5688190bd5e41d4d8189668 completed April 29, 2026, 1:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b0c8433348190894a06439b3461ed completed May 18, 2026, 12:56 p.m.
NEDg Description generation batch_6a0b0f24bb9c81909d2e5b08ff081ad0 completed May 18, 2026, 1:07 p.m.
NED2 Entity disambiguation (via description) batch_6a0b100675688190b493cc73e0d778fc completed May 18, 2026, 1:11 p.m.
Created at: April 16, 2026, 8:48 p.m.