Triple
T3234514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Killingworth Colliery |
E67819
|
entity |
| Predicate | railwayGaugeContext |
P47503
|
FINISHED |
| Object | early colliery wagonways |
—
|
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: early colliery wagonways | Statement: [Killingworth Colliery, railwayGaugeContext, early colliery wagonways]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: railwayGaugeContext Context triple: [Killingworth Colliery, railwayGaugeContext, early colliery wagonways]
-
A.
usesRailGauge
Indicates that one entity (typically a railway system or line) operates using the specified rail gauge measurement of the other entity.
-
B.
railroadMet
Indicates that two or more railroads encountered or connected with each other at a specific place or time.
-
C.
railcode
Indicates that an entity is associated with a specific railway code used for identification or classification within a rail system.
-
D.
railwayUse
Indicates that something is used as, or functions in the capacity of, a railway or rail-based transportation facility.
-
E.
railTracks
Indicates that one entity consists of, includes, or is associated with rail tracks used for guiding trains or rail vehicles.
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaedcd9588190b3623f0109d653a4 |
completed | March 8, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69ada4159e0481908cbbdd750f5e08c7 |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada698eeb48190a1f5762fdd3b7b63 |
completed | March 8, 2026, 4:40 p.m. |
Created at: March 8, 2026, 3:08 p.m.