Triple
T69640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dunfermline Queen Margaret railway station |
E1391
|
entity |
| Predicate | hasLoudspeakerAnnouncements |
P182
|
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: [Dunfermline Queen Margaret railway station, hasLoudspeakerAnnouncements, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLoudspeakerAnnouncements Context triple: [Dunfermline Queen Margaret railway station, hasLoudspeakerAnnouncements, yes]
-
A.
hasAudience
Indicates that an entity is intended to be received, viewed, or engaged with by a particular group of people.
-
B.
hasSpokenAbout
Indicates that one entity has verbally expressed, discussed, or mentioned another entity or topic.
-
C.
hasFeature
chosen
Indicates that an entity possesses, exhibits, or includes a particular characteristic, attribute, or component.
-
D.
hasFaregates
Indicates that an entity is equipped with or contains faregates used to control or validate access, typically for paid entry.
-
E.
hasEmergencyServices
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
- 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a24fd16c248190a6ee4cd96c388772 |
completed | Feb. 28, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69a24eaa0df88190add55579b2b9fd02 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.