Triple

T525334
Position Surface form Disambiguated ID Type / Status
Subject LFPG E10903 entity
Predicate terminal P11513 FINISHED
Object Terminal 3 E18505 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 3 | Statement: [LFPG, terminal, Terminal 3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terminal 3
Context triple: [LFPG, terminal, Terminal 3]
  • A. Terminal 3
    Terminal 3 is the main international passenger terminal at José Martí International Airport in Havana, Cuba, handling most long-haul and major airline operations.
  • B. Terminal 3
    Terminal 3 is one of the passenger terminals at Manchester Airport, serving a range of domestic and international flights with dedicated check-in, security, and boarding facilities.
  • C. Terminal 3 chosen
    Terminal 3 is one of the passenger terminals at Paris Charles de Gaulle Airport, primarily serving low-cost and charter airlines.
  • D. Terminal 3
    Terminal 3 is one of the main passenger terminals at Phoenix Sky Harbor International Airport, serving as a hub for multiple domestic and some international flights with modernized facilities and amenities.
  • E. Terminal 3
    Terminal 3 is a major passenger terminal at Wuhan Tianhe International Airport, serving as a key hub for domestic and international air travel in central China.
  • 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_69a2e84b16c4819088d284c47c3a7968 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f1b7f448819087e5e7f3b37d7142 completed Feb. 28, 2026, 1:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4e62e2b3c81908215dab8c0717495 completed March 2, 2026, 1:21 a.m.
Created at: Feb. 28, 2026, 1:12 p.m.