Triple
T3910897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nijmegen Lent railway station |
E87317
|
entity |
| Predicate | hasWaitingShelter |
P3789
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Nijmegen Lent railway station, hasWaitingShelter, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWaitingShelter Context triple: [Nijmegen Lent railway station, hasWaitingShelter, yes]
-
A.
hasShelters
chosen
Indicates that one entity provides, contains, or is associated with one or more shelters for another entity or purpose.
-
B.
hasWaitingArea
Indicates that an entity provides or includes a designated space where people can wait before receiving a service or proceeding to another area.
-
C.
hasNearbySanctuary
Indicates that one entity has a sanctuary or place of refuge located close to it in space or distance.
-
D.
hasRoom
Indicates that an entity possesses, contains, or is associated with a specific room.
-
E.
hasWarden
Indicates that one entity serves as the warden or supervisory authority responsible for another 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1abe2dc81909c18aeae9b286898 |
completed | March 9, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69aee75cff148190b6d5979d17fae085 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:22 p.m.