Triple

T3997844
Position Surface form Disambiguated ID Type / Status
Subject Aéroports de Paris E87140 entity
Predicate alsoKnownAs P39 FINISHED
Object ADP E11760 NE FINISHED

How this triple was built (2 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: ADP | Statement: [Aéroports de Paris, alsoKnownAs, ADP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ADP
Context triple: [Aéroports de Paris, alsoKnownAs, ADP]
  • A. Groupe ADP chosen
    Groupe ADP is a major French airport management company that owns and operates the Paris-area airports and provides aviation and related services worldwide.
  • B. Workday
    Workday is a cloud-based enterprise software company best known for its human capital management and financial management applications used by large organizations worldwide.
  • C. ADBP
    ADBP is the former name of Pakistan's state-owned agricultural development bank, which provides financial services and credit to support the country's farming and rural sectors.
  • D. Paylocity
    Paylocity is a U.S.-based provider of cloud payroll and human capital management software solutions for businesses.
  • E. EMP
    EMP is a Seattle-based museum and cultural institution (now called the Museum of Pop Culture) dedicated to contemporary popular music, science fiction, and pop culture.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69aed94118148190975e6aa4e554cde9 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa3ef7ac8190abe02f440ff83c43 completed March 9, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b54c54f05c8190b18c2d4839a61b64 completed March 14, 2026, 11:53 a.m.
Created at: March 9, 2026, 3:34 p.m.