Triple

T20643178
Position Surface form Disambiguated ID Type / Status
Subject Hannover Airport E507281 entity
Predicate hasPassengerTerminal P1297 FINISHED
Object Terminal A NE NERFINISHED

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 A | Statement: [Hannover Airport, hasPassengerTerminal, Terminal A]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terminal A
Context triple: [Hannover Airport, hasPassengerTerminal, Terminal A]
  • A. Terminal A
    Terminal A was one of the original passenger terminals at Shenzhen Bao’an International Airport, later decommissioned and demolished as the airport expanded and modernized its facilities.
  • B. Terminal A chosen
    Terminal A is one of the passenger terminals at Hannover Airport in Germany, serving airline check-in, departures, and arrivals operations.
  • C. Terminal A
    Terminal A is a major passenger terminal at Abu Dhabi International Airport, serving as a key hub for international and regional flights.
  • D. Terminal A
    Terminal A is one of the passenger terminals at Sheremetyevo International Airport in Moscow, serving specific flights and airlines within the airport’s terminal complex.
  • E. Terminal A
    Terminal A is one of the main passenger terminals at O. R. Tambo International Airport in Johannesburg, serving as a key hub for airline check-in, departures, and arrivals.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4be702c8190a3d2410a881d310a completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6af1c51f48190abba54a5aace9fc8 completed April 20, 2026, 10:56 p.m.
Created at: April 16, 2026, 11:43 a.m.