Triple

T14601189
Position Surface form Disambiguated ID Type / Status
Subject Terminal 2 complex E342707 entity
Predicate hasPart P35 FINISHED
Object Terminal 2F E68666 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: Terminal 2F | Statement: [Terminal 2 complex, hasPart, Terminal 2F]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terminal 2F
Context triple: [Terminal 2 complex, hasPart, Terminal 2F]
  • A. Terminal 2F chosen
    Terminal 2F is one of the passenger terminals at Paris Charles de Gaulle Airport, primarily serving international flights with dedicated check-in, security, and boarding facilities.
  • B. Terminal 2C
    Terminal 2C is one of the passenger terminals at Paris Charles de Gaulle Airport, serving various international and European flights with check-in, boarding, and arrival facilities.
  • C. Terminal 2C
    Terminal 2C is one of the sub-terminals within Barcelona–El Prat Airport’s Terminal 2 complex, serving as a dedicated passenger facility for specific airlines and flights.
  • D. Terminal 2A
    Terminal 2A is one of the passenger terminals at Paris Charles de Gaulle Airport, serving various international and European flights with check-in, boarding, and arrival facilities.
  • E. Terminal 2A
    Terminal 2A is one of the main passenger terminals at Budapest Ferenc Liszt International Airport, serving a significant share of its commercial airline traffic.
  • 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_69d822dec68081908c2553145c4051dc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb438748081908020ce04b869866a completed April 14, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fde169eb6481909d7fac6d984a2af1 completed May 8, 2026, 1:13 p.m.
Created at: April 10, 2026, 1:25 a.m.