Triple
T23452035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bedum |
E567806
|
entity |
| Predicate | hasFormerMunicipalityCode |
P83825
|
FINISHED |
| Object | 0005 |
—
|
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: 0005 | Statement: [Bedum, hasFormerMunicipalityCode, 0005]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFormerMunicipalityCode Context triple: [Bedum, hasFormerMunicipalityCode, 0005]
-
A.
formerMunicipalityOf
Indicates that an entity was previously an independent municipality that has since been merged into or replaced by the referenced municipality.
-
B.
hasFormerCounty
Indicates that an entity was previously part of, or administered by, a particular county in the past but no longer is.
-
C.
formerMunicipalityType
Indicates that an entity was previously classified as a specific type of municipality but no longer holds that municipal status.
-
D.
formerMunicipalityNumber
chosen
Indicates that an entity was previously assigned a specific official municipality identification number before administrative or territorial changes.
-
E.
previousMunicipalityNameVariant
Indicates that an entity had a different municipality name in the past, specifying a former variant of its municipal designation.
- 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_69e2458b4c888190b1d7998f9862a558 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64ded5c8190bd50ac5b9bbb0f5f |
completed | April 29, 2026, 6:33 a.m. |
| PD | Predicate disambiguation | batch_69f06201d33481909b5fd7b92d03e658 |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 5:52 p.m.