Triple
T34509981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schlossplatz (Wiesbaden) |
E885991
|
entity |
| Predicate | nearbyBusStop |
P15438
|
FINISHED |
| Object | Schlossplatz bus stop |
—
|
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: Schlossplatz bus stop | Statement: [Schlossplatz (Wiesbaden), nearbyBusStop, Schlossplatz bus stop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyBusStop Context triple: [Schlossplatz (Wiesbaden), nearbyBusStop, Schlossplatz bus stop]
-
A.
nearbyHeritageStation
Indicates that one entity is located close to a heritage (historically or culturally significant) station.
-
B.
nearbyTerminus
Indicates that one terminus (end point or final stop) is located close to another terminus in space.
-
C.
hasPublicTransportStop
chosen
Indicates that a location or area contains or is served by a public transport stop, such as a bus, tram, or train stop.
-
D.
hasNearbyTramStop
Indicates that a location has a tram stop situated within a short walking distance or close proximity.
-
E.
nearbyLRTStation
Indicates that there is an LRT (light rail transit) station located close to the referenced place or entity.
- 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_69f349cc0220819081f154c6964f4dc2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fe7b1c506c8190869c1a22031e0571 |
completed | May 9, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69fe796b2bdc8190a86980d44008f875 |
completed | May 9, 2026, 12:01 a.m. |
Created at: May 1, 2026, 2:01 a.m.