Triple
T22548717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saundersfoot railway station |
E557498
|
entity |
| Predicate | hasLineUsage |
P85306
|
FINISHED |
| Object | regional passenger traffic |
—
|
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: regional passenger traffic | Statement: [Saundersfoot railway station, hasLineUsage, regional passenger traffic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLineUsage Context triple: [Saundersfoot railway station, hasLineUsage, regional passenger traffic]
-
A.
usesLineCode
Indicates that one entity employs or references a specific line code as part of its operation, identification, or communication.
-
B.
hasUsageLevel
chosen
Indicates the degree or intensity with which something is used or utilized.
-
C.
hasDeFactoLine
Indicates that there exists an unofficial or non-legally recognized boundary or demarcation line functioning in practice between the related entities.
-
D.
usedByLine
Indicates that something (such as a resource, tool, or component) is utilized or operated by a particular line (for example, a production line, transit line, or code line).
-
E.
usesLineCharacteristic
Indicates that one entity employs or is based on a specific characteristic or property of a line.
- 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_69e11e58662081909ae346ab384514ca |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f372df48190ae5dd461b1230bc7 |
completed | April 29, 2026, 1:30 a.m. |
| PD | Predicate disambiguation | batch_69e898cb3fb48190add6ab24a2df5822 |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:52 p.m.