Triple
T18462672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Italian regions |
E451076
|
entity |
| Predicate | numberOfOrdinaryStatuteRegions |
P131754
|
FINISHED |
| Object | 15 |
—
|
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: 15 | Statement: [Italian regions, numberOfOrdinaryStatuteRegions, 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfOrdinaryStatuteRegions Context triple: [Italian regions, numberOfOrdinaryStatuteRegions, 15]
-
A.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
B.
numberOfJurisdictions
Indicates the count of distinct legal or administrative jurisdictions associated with or applicable to an entity or situation.
-
C.
hasNumberOfProvinces
Indicates the total count of provinces associated with a given entity.
-
D.
numberOfRegionalCouncils
Indicates the total count of regional councils associated with a given entity.
-
E.
hasNumberOfStatesAndDistricts
Indicates a relationship where an entity is associated with a specific count of its constituent states and districts.
- F. None of above. chosen
Provenance (4 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_69d8d38345688190b565eac2e4cd7935 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e52a80a2bc81909ec14811577a311d |
completed | April 19, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69e469d05cf4819099baf1665a9cf18a |
completed | April 19, 2026, 5:36 a.m. |
| PDg | Predicate description generation | batch_69e46d2aa72c8190a40854a7a52081e2 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 11:33 a.m.