Triple
T19778242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pembrey and Burry Port railway station |
E475062
|
entity |
| Predicate | hasLineNumberOfTracks |
P1707
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Pembrey and Burry Port railway station, hasLineNumberOfTracks, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLineNumberOfTracks Context triple: [Pembrey and Burry Port railway station, hasLineNumberOfTracks, 2]
-
A.
numberOfTracks
chosen
Indicates the quantity of tracks associated with a given entity.
-
B.
isTrackNumberUnknown
Indicates that the track number associated with an item or entity is not known or has not been specified.
-
C.
hasTrackFeatures
Indicates that something possesses or is associated with specific track-related characteristics or attributes.
-
D.
hasTwoTracks
Indicates that the subject possesses or is associated with exactly two distinct tracks or pathways.
-
E.
hasTrack
Indicates that one entity possesses, includes, or is associated with a specific track (such as a path, course, or recorded item).
- 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_69d8e51a43a08190956bc6df13c91a77 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65361a8e08190b52944aab25e45fa |
completed | April 20, 2026, 4:25 p.m. |
| PD | Predicate disambiguation | batch_69e53053ed2881908400becdfada7fd3 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:48 p.m.