Triple
T2897821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ZA-NC |
E63983
|
entity |
| Predicate | codeForSubdivisionType |
P9832
|
FINISHED |
| Object | province |
—
|
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: province | Statement: [ZA-NC, codeForSubdivisionType, province]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: codeForSubdivisionType Context triple: [ZA-NC, codeForSubdivisionType, province]
-
A.
hasSubdivisionCode
Indicates that an entity is associated with a specific code identifying one of its internal subdivisions (such as a state, province, or region).
-
B.
hasSubdivisionCodeContext
Indicates that a subdivision code is interpreted within a specific coding or contextual framework that defines its meaning.
-
C.
hasTypeOfSubdivision
Indicates that one administrative or territorial unit is classified as a specific kind or category of subdivision.
-
D.
countrySubdivisionType
chosen
Indicates the specific type or category of an administrative or territorial subdivision within a country (e.g., state, province, region).
-
E.
subregionCodeFor
Indicates that one entity is the code assigned to identify a specific subregion of the other entity.
- 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_69ab4c45822c8190830c5f2bb97bcfd0 |
completed | March 6, 2026, 9:51 p.m. |
| NER | Named-entity recognition | batch_69abe08fe3248190a6bb7de2a2c317b1 |
completed | March 7, 2026, 8:23 a.m. |
| PD | Predicate disambiguation | batch_69abdd17bcdc8190aa47274a50ba4ad4 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:08 p.m.