Triple
T10852283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2007 Danish Municipal Reform |
E256176
|
entity |
| Predicate | previousNumberOfMunicipalities |
P24416
|
FINISHED |
| Object | 271 |
—
|
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: 271 | Statement: [2007 Danish Municipal Reform, previousNumberOfMunicipalities, 271]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: previousNumberOfMunicipalities Context triple: [2007 Danish Municipal Reform, previousNumberOfMunicipalities, 271]
-
A.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
B.
formerMunicipalityNumber
Indicates that an entity was previously assigned a specific official municipality identification number before administrative or territorial changes.
-
C.
previousMunicipalityNameVariant
Indicates that an entity had a different municipality name in the past, specifying a former variant of its municipal designation.
-
D.
previousNumberOfMembers
Indicates the number of members an entity had at an earlier or prior point in time.
-
E.
previousNumberOfConstituents
chosen
Indicates the number of constituents an entity had at an earlier point in time, before its current state.
- 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_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. |
Created at: April 8, 2026, 9:20 p.m.