Triple
T10852287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2007 Danish Municipal Reform |
E256176
|
entity |
| Predicate | numberOfNewRegions |
P96073
|
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: [2007 Danish Municipal Reform, numberOfNewRegions, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfNewRegions Context triple: [2007 Danish Municipal Reform, numberOfNewRegions, 5]
-
A.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
B.
usedRegion
Indicates that an entity operates in, applies to, or is utilized within a specified geographic or administrative region.
-
C.
numberOfProvinces
Indicates the total count of provinces associated with a given entity or within a specified region or country.
-
D.
modernRegion
Indicates that an entity is located within or associated with a contemporary, present-day geographic or administrative region.
-
E.
numberOfDistricts
Indicates the total count of districts associated with a given entity or area.
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75117b76c8190b0fb216b1428c3c7 |
completed | April 9, 2026, 7:11 a.m. |
| PD | Predicate disambiguation | batch_69d70d2b51448190bae748ed6c23edde |
completed | April 9, 2026, 2:21 a.m. |
| PDg | Predicate description generation | batch_69d7101c96708190808fef73199e8482 |
completed | April 9, 2026, 2:34 a.m. |
Created at: April 8, 2026, 9:20 p.m.