Triple
T1139684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oslo Tramway |
E23420
|
entity |
| Predicate | numberOfStops |
P1301
|
FINISHED |
| Object | over 100 stops |
—
|
LITERAL 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: over 100 stops | Statement: [Oslo Tramway, numberOfStops, over 100 stops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStops Context triple: [Oslo Tramway, numberOfStops, over 100 stops]
-
A.
numberOfStations
chosen
Indicates the total count of stations associated with or contained by a given entity.
-
B.
isOneOfBusiestStopsOn
Indicates that a stop ranks among the most heavily used or frequently served stops on a given route or line.
-
C.
hasIntermediateStation
Indicates that a route, journey, or connection includes a station that lies between its starting point and its final destination.
-
D.
numberOfFlights
Indicates the total count of flights associated with a given entity or within a specified context.
-
E.
maximumStationsPerSegment
Indicates the greatest number of stations that are allowed or can exist within a single segment.
- 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_69a493ef399c8190b04b9146d2314f59 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bde18d208190848c189b2b8d585f |
completed | March 1, 2026, 10:29 p.m. |
| PD | Predicate disambiguation | batch_69a4bb4b52d48190bec2e7ad1cc8efc0 |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:44 p.m.