Triple
T8719891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French regions |
E206984
|
entity |
| Predicate | previousNumberOfRegions |
P24416
|
FINISHED |
| Object | 22 metropolitan regions |
—
|
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: 22 metropolitan regions | Statement: [French regions, previousNumberOfRegions, 22 metropolitan regions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: previousNumberOfRegions Context triple: [French regions, previousNumberOfRegions, 22 metropolitan regions]
-
A.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
B.
previouslyContainedRegion
Indicates that one region used to spatially contain another region at some earlier time, but does not necessarily do so now.
-
C.
previousNumberOfConstituents
chosen
Indicates the number of constituents an entity had at an earlier point in time, before its current state.
-
D.
previousNumberOfMembers
Indicates the number of members an entity had at an earlier or prior point in time.
-
E.
regionNumber
Indicates that an entity is assigned to or associated with a specific numbered region within a larger spatial or organizational division.
- 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_69ca835811d8819081ea00fd2a2c9a1c |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d02a52c81909f93622ae6920b80 |
completed | March 31, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_69cc457093188190959287a6458651c6 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:36 p.m.