Triple

T10700501
Position Surface form Disambiguated ID Type / Status
Subject Logan Terminal E E252263 entity
Predicate alsoKnownAs P39 FINISHED
Object Terminal E E50180 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 E | Statement: [Logan Terminal E, alsoKnownAs, Terminal E]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Terminal E
Context triple: [Logan Terminal E, alsoKnownAs, Terminal E]
  • A. Terminal E
    Terminal E is an international satellite terminal at Zurich Airport primarily serving long-haul and non-Schengen flights.
  • B. Terminal E
    Terminal E is one of the passenger terminals at Sheremetyevo International Airport in Moscow, serving international flights with modern facilities and connections to adjacent terminals.
  • C. Terminal E chosen
    Terminal E is the international terminal at Boston Logan International Airport, serving most of the airport’s overseas flights and customs operations.
  • D. Terminal E
    Terminal E is one of the passenger terminals at Dallas/Fort Worth International Airport, serving various domestic and some international flights with multiple gates and amenities.
  • E. Terminal E
    Terminal E is one of the passenger terminals at Philadelphia International Airport, primarily serving domestic airline operations 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_69d6aa5cbabc8190973e683950d89faf completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6fd8bb7408190a350840e1df3b910 completed April 9, 2026, 1:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69d998ed78e481908537ae10d55e6f65 completed April 11, 2026, 12:42 a.m.
Created at: April 8, 2026, 9:12 p.m.