Triple

T11697364
Position Surface form Disambiguated ID Type / Status
Subject Floral Park station E278030 entity
Predicate code P1537 FINISHED
Object FP
FP is the station code for Floral Park station on the Long Island Rail Road in New York.
 E939637 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: [Floral Park station, code, FP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FP
Context triple: [Floral Park station, code, FP]
  • A. PF
    PF is the vehicle registration code used on license plates for the German city of Pforzheim.
  • B. FPF
    FPF is the Portuguese Football Federation, the national governing body responsible for organizing and overseeing football in Portugal, including its national teams.
  • C. FO
    FO is the IATA airline designator assigned to Flybondi, a low-cost carrier based in Argentina.
  • D. FW
    FW refers to the Free Voters (Freie Wähler), a German political association and party known for its strong local-government focus and presence in Bavarian municipal and regional politics.
  • E. FW
    FW is the IATA airline designator assigned to the Japanese regional carrier Ibex Airlines.
  • 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: [Floral Park station, code, FP]
Generated description
FP is the station code for Floral Park station on the Long Island Rail Road in New York.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FP
Target entity description: FP is the station code for Floral Park station on the Long Island Rail Road in New York.
  • A. PF
    PF is the vehicle registration code used on license plates for the German city of Pforzheim.
  • B. FPF
    FPF is the Portuguese Football Federation, the national governing body responsible for organizing and overseeing football in Portugal, including its national teams.
  • C. FO
    FO is the IATA airline designator assigned to Flybondi, a low-cost carrier based in Argentina.
  • D. FW
    FW refers to the Free Voters (Freie Wähler), a German political association and party known for its strong local-government focus and presence in Bavarian municipal and regional politics.
  • E. FW
    FW is the IATA airline designator assigned to the Japanese regional carrier Ibex Airlines.
  • 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_69d6aafe02d881909900d54ad7d4af84 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a47cef60819088b7cc3a3a711e4c completed April 10, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef147e2e10819085eaed83fd955b6b completed April 27, 2026, 7:47 a.m.
NEDg Description generation batch_69ef3553a1748190b554463bcea8bd1d completed April 27, 2026, 10:07 a.m.
NED2 Entity disambiguation (via description) batch_69ef51fe3824819099f440426d3e6888 completed April 27, 2026, 12:09 p.m.
Created at: April 8, 2026, 9:40 p.m.