Triple
T23108308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harda railway station |
E576237
|
entity |
| Predicate | hasAutoStandNearby |
P150945
|
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: [Harda railway station, hasAutoStandNearby, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAutoStandNearby Context triple: [Harda railway station, hasAutoStandNearby, yes]
-
A.
hasOpenSpaceNearby
Indicates that an entity is located near or adjacent to an area that is open, unobstructed, or undeveloped.
-
B.
hasStand
Indicates that an entity possesses, is equipped with, or is supported by a stand or base structure.
-
C.
hasNearbyBay
Indicates that one entity is located close to or adjacent to a bay associated with the other entity.
-
D.
hasNearbyMode
Indicates that one entity has another entity located close enough to be considered in its immediate vicinity or surrounding area.
-
E.
hasNearbyAccess
Indicates that one entity has convenient, close-proximity access to another resource, service, or location.
- F. None of above. chosen
Provenance (4 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_69e245f4af548190898d434a64a1e774 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e0c7b9c8190b1160485eae87c9b |
completed | April 29, 2026, 4:50 a.m. |
| PD | Predicate disambiguation | batch_69ef89f020588190b43393e048e7eda3 |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 3:58 p.m.