Triple
T12504692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Region II |
E298918
|
entity |
| Predicate | hasProvinceCount |
P3455
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Region II, hasProvinceCount, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProvinceCount Context triple: [Region II, hasProvinceCount, 5]
-
A.
hasNumberOfProvinces
chosen
Indicates the total count of provinces associated with a given entity.
-
B.
numberOfProvinces
Indicates the total count of provinces associated with a given entity or within a specified region or country.
-
C.
hasProvinceStatus
Indicates that an entity holds the administrative or political status of a province within a larger territorial or governmental system.
-
D.
hasProvinceOrRegion
Indicates that one entity includes, is associated with, or is located within a specific province or region as an administrative or geographic subdivision.
-
E.
hasNumberOfCounties
Indicates the relationship that specifies how many counties are associated with or contained within a given 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_69d6ada4cd388190ae3bbf83ff87057a |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94dfddf38819099263b8b1e804736 |
completed | April 10, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_69d94d43b7008190af2648fe09fd6d23 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:57 p.m.