Triple
T19109395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kilkenny railway station |
E467745
|
entity |
| Predicate | hasPassengerBuildingMaterial |
P37535
|
FINISHED |
| Object | stone |
—
|
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: stone | Statement: [Kilkenny railway station, hasPassengerBuildingMaterial, stone]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerBuildingMaterial Context triple: [Kilkenny railway station, hasPassengerBuildingMaterial, stone]
-
A.
hasStationBuildingMaterial
chosen
Indicates that a station’s building is constructed from, or primarily composed of, a specified material.
-
B.
hasBuildingMaterialInHut
Indicates that a hut is constructed using a specified building material.
-
C.
hasArchitecturalMaterial
Indicates that something is constructed from, incorporates, or is characterized by a particular architectural material.
-
D.
مادة البناء
Indicates a relationship where something serves as, or is used as, a construction material in building or structural work.
-
E.
hasCrewCompartmentMaterial
Indicates that an entity’s crew compartment is made of, or incorporates, a specified material.
- 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_69d8dd06a26481908039e2a1bae8c597 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e392ef488190a230b1d2890b2a9d |
completed | April 20, 2026, 8:28 a.m. |
| PD | Predicate disambiguation | batch_69e4b9ac41848190afd0f33b42cebe99 |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 12:04 p.m.