Triple
T25365032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ID-YO |
E636076
|
entity |
| Predicate | regionTypeInCountryContext |
P62377
|
FINISHED |
| Object | province-level unit |
—
|
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-level unit | Statement: [ID-YO, regionTypeInCountryContext, province-level unit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionTypeInCountryContext Context triple: [ID-YO, regionTypeInCountryContext, province-level unit]
-
A.
regionTypeOfPlace
Indicates that a place belongs to or is categorized under a specific type of geographic or administrative region.
-
B.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
C.
regionalType
Indicates the classification of a region according to its designated type or category within a broader geographic or administrative system.
-
D.
populationRegionType
Indicates the type or category of region (e.g., city, state, country) to which a given population value or statistic applies.
-
E.
politicalRegionType
chosen
Indicates the classification of a political region according to its governmental or administrative type (e.g., state, province, municipality).
- 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_69e75a9b7cf481909f2dcdfb37d95ca7 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f67c9fe7b48190b79b4041357edb49 |
completed | May 2, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69f678cc272081909e5c70f1bc7407f0 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 21, 2026, 1:36 p.m.