Triple
T845624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Region |
E18270
|
entity |
| Predicate | oneOfNumberOfRegions |
P2355
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [South Region, oneOfNumberOfRegions, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oneOfNumberOfRegions Context triple: [South Region, oneOfNumberOfRegions, 4]
-
A.
numberOfRegions
chosen
Indicates the total count of distinct regions associated with or contained within a given entity.
-
B.
hasNumberOfProvinces
Indicates the total count of provinces associated with a given entity.
-
C.
regionNumber
Indicates that an entity is assigned to or associated with a specific numbered region within a larger spatial or organizational division.
-
D.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
-
E.
numberOfProvinces
Indicates the total count of provinces associated with a given entity or within a specified region or country.
- 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac0a91e48190b4349ae8bb67fd90 |
completed | March 1, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7f396c8190aa8b8dfbe0b4f732 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.