Triple
T20943888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winchcombe railway station |
E515792
|
entity |
| Predicate | hasWaterColumnOrTower |
P8955
|
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: [Winchcombe railway station, hasWaterColumnOrTower, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWaterColumnOrTower Context triple: [Winchcombe railway station, hasWaterColumnOrTower, yes]
-
A.
hasWaterChannel
Indicates that one entity possesses or contains a channel or conduit through which water flows or is transported.
-
B.
waterTowerBuiltIn
Indicates that a water tower was constructed in a specific location or during a specific time period.
-
C.
hasDomeOrTower
Indicates that one entity possesses or features a dome or a tower as part of its structure.
-
D.
hasTower
chosen
Indicates that one entity possesses, contains, or is characterized by the presence of a tower.
-
E.
hasWaterFeatures
Indicates that an entity includes or is associated with water-related elements such as fountains, ponds, streams, or similar features.
- 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_69e0b4fc13408190b06868df03c5c29b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6f95838708190978dfa8bc786fb22 |
completed | April 21, 2026, 4:13 a.m. |
| PD | Predicate disambiguation | batch_69e5c9b1bae48190a845165fed1b005e |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:52 p.m.