Triple

T1335464
Position Surface form Disambiguated ID Type / Status
Subject Chestnut Hill East Line E28737 entity
Predicate connectsWith P37 FINISHED
Object Airport Line E16837 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: Airport Line | Statement: [Chestnut Hill East Line, connectsWith, Airport Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Airport Line
Context triple: [Chestnut Hill East Line, connectsWith, Airport Line]
  • A. Airport Line
    The Airport Line is a Manchester Metrolink light rail route that connects central Manchester with Manchester Airport, serving key suburbs and transport interchanges along the way.
  • B. Airport Line chosen
    Airport Line is a SEPTA Regional Rail service in the Philadelphia area that connects Center City with Philadelphia International Airport.
  • C. Airport Line
    Airport Line is a Wuhan Metro route that connects the city’s urban rail network with its main airport, providing rapid transit access for air travelers.
  • D. Flughafen
    Flughafen is the Nuremberg U-Bahn station that serves Nuremberg Airport, providing direct metro access between the airport and the city.
  • E. CNN Airport Network
    CNN Airport Network was a specialized television channel from CNN that provided curated news, weather, and travel-related programming exclusively to airport terminals across the United States.
  • 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_69a498561a508190a3e1bc137c2b866a completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c1ecb5208190a9eadda113c91e66 completed March 1, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69acc62b9bd081909dbe22cbea03f21f completed March 8, 2026, 12:43 a.m.
Created at: March 1, 2026, 7:55 p.m.