Triple
T23716945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lehrter Bahnhof |
E586031
|
entity |
| Predicate | hadStationBuildingType |
P142313
|
FINISHED |
| Object | terminal station |
—
|
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: terminal station | Statement: [Lehrter Bahnhof, hadStationBuildingType, terminal station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadStationBuildingType Context triple: [Lehrter Bahnhof, hadStationBuildingType, terminal station]
-
A.
hasStationBuilding
Indicates that a station is associated with or includes a station building as part of its facilities.
-
B.
hasStationHouseType
chosen
Indicates the specific type or classification of a station house associated with an entity.
-
C.
hasStationBuildingMaterial
Indicates that a station’s building is constructed from, or primarily composed of, a specified material.
-
D.
hasStationTypeAt
Indicates that a specific type of station is present or assigned at a particular location or point.
-
E.
hasStationHall
Indicates that one entity (typically a station) includes or is associated with a station hall area as part of its structure or facilities.
- 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_69e24906fb108190a6898751e46bdc11 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b77c1de881909614988c7d0d1400 |
completed | April 29, 2026, 7:47 a.m. |
| PD | Predicate disambiguation | batch_69f155e4b1148190836ede4741dcb888 |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 6:54 p.m.