Triple
T36396135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gran Via de les Corts Catalanes |
E896484
|
entity |
| Predicate | hasMetroStationOnOrAlong |
P158757
|
FINISHED |
| Object | Espanya station |
—
|
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: Espanya station | Statement: [Gran Via de les Corts Catalanes, hasMetroStationOnOrAlong, Espanya station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMetroStationOnOrAlong Context triple: [Gran Via de les Corts Catalanes, hasMetroStationOnOrAlong, Espanya station]
-
A.
connectsToMetroLineAt
Indicates that one location or transportation facility is directly linked or provides access to a specific metro line at a particular point.
-
B.
nearMetroStation
Indicates that one entity is located close to or within a short walking distance of a metro (subway) station.
-
C.
hasMetroTerminus
Indicates that one location serves as the terminal (end) station of a metro line for another location.
-
D.
hasMetroStationArea
chosen
Indicates that a specified area contains or is served by a metro (subway) station.
-
E.
hasPublicTransportStop
Indicates that a location or area contains or is served by a public transport stop, such as a bus, tram, or train stop.
- F. None of above.
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_69f76e52e3108190becf70b090ae7bd6 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ff5b233e9c8190adc06cca0758986b |
completed | May 9, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69ff5a5682108190a006b23c4fcdcc7c |
completed | May 9, 2026, 4:01 p.m. |
Created at: May 3, 2026, 4:10 p.m.