Triple
T10159849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Britain B roads |
E233859
|
entity |
| Predicate | usesNumberingZones |
P6793
|
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: [Great Britain B roads, usesNumberingZones, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesNumberingZones Context triple: [Great Britain B roads, usesNumberingZones, yes]
-
A.
numberOfZones
Indicates the quantity of distinct zones associated with or contained by a given entity.
-
B.
usesHarmonizedNumberingWith
Indicates that two entities apply the same standardized numbering scheme so their identifiers or codes are directly comparable or aligned.
-
C.
railwayZoneNumber
Indicates the specific numbered zone of a railway network within which the referenced entity is located or classified.
-
D.
numberOfThemedZones
Indicates the total count of distinct themed zones associated with or contained within an entity.
-
E.
hasZone
chosen
Indicates that one entity possesses, contains, or is associated with a specific zone or designated area.
- 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_69ca848e80748190b91d1e04d35512c7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec5831e481909b48fdfa8f1c670b |
completed | April 2, 2026, 4:11 a.m. |
| PD | Predicate disambiguation | batch_69cd4ba795808190acc9124c98c6e40f |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:09 p.m.