Triple
T69634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dunfermline Queen Margaret railway station |
E1391
|
entity |
| Predicate | hasSignageLanguage |
P2177
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Dunfermline Queen Margaret railway station, hasSignageLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSignageLanguage Context triple: [Dunfermline Queen Margaret railway station, hasSignageLanguage, English]
-
A.
hasSignificantLanguage
Indicates that an entity possesses a language that plays an important or primary role in its communication, identity, or functioning.
-
B.
signageStandard
Indicates that something conforms to, follows, or specifies a particular standard or convention for signage.
-
C.
languageOfCeremony
Indicates the language in which a ceremony is conducted or officially performed.
-
D.
hasLanguageGroup
Indicates that an entity belongs to, is associated with, or is categorized under a particular language group.
-
E.
includesLanguage
chosen
Indicates that one entity contains, supports, or makes use of a specified language as part of its content, functionality, or representation.
- 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.