Triple
T1306030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metrolink tram network |
E27879
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object | Airport Line |
E5769
|
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: [Metrolink tram network, hasLine, Airport Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Airport Line Context triple: [Metrolink tram network, hasLine, Airport Line]
-
A.
Airport Line
chosen
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
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.
-
C.
Airport Line
Airport Line is a SEPTA Regional Rail service in the Philadelphia area that connects Center City with Philadelphia International Airport.
-
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_69a496d7d83481908f83085854e51328 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c13524d481909e8f5bb2ab91f6e4 |
completed | March 1, 2026, 10:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acb306e3cc8190997cda8aaedbcebb |
completed | March 7, 2026, 11:21 p.m. |
Created at: March 1, 2026, 7:51 p.m.