Triple

T14601190
Position Surface form Disambiguated ID Type / Status
Subject Terminal 2 complex E342707 entity
Predicate hasPart P35 FINISHED
Object Terminal 2G E99006 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 2G | Statement: [Terminal 2 complex, hasPart, Terminal 2G]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terminal 2G
Context triple: [Terminal 2 complex, hasPart, Terminal 2G]
  • A. Terminal 2G chosen
    Terminal 2G is a regional satellite terminal at Paris Charles de Gaulle Airport primarily serving short-haul European flights operated by Air France and its partners.
  • B. Terminal 2 GIG
    Terminal 2 GIG is a passenger terminal at Rio de Janeiro–Galeão International Airport that handles a significant share of the airport’s domestic and international flights.
  • C. Terminal 2D
    Terminal 2D is a passenger terminal at Paris Charles de Gaulle Airport, serving as one of the facilities handling flights and travelers at this major international hub.
  • D. Terminal 2
    Terminal 2 is one of the main passenger terminals at Nice Côte d’Azur Airport, handling a large share of its domestic and international flights.
  • E. Terminal 2
    Terminal 2 is one of the main passenger terminals at Cairo International Airport, serving a mix of international and regional flights.
  • 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_69fd94cc9fbc819090ae4efe9bc618aa completed May 8, 2026, 7:46 a.m.
Created at: April 10, 2026, 1:25 a.m.