Triple
T18402301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rawene |
E450024
|
entity |
| Predicate | hasCensusAreaUnit |
P85312
|
FINISHED |
| Object | Rawene census area unit |
—
|
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: Rawene census area unit | Statement: [Rawene, hasCensusAreaUnit, Rawene census area unit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCensusAreaUnit Context triple: [Rawene, hasCensusAreaUnit, Rawene census area unit]
-
A.
hasCensusAreas
chosen
Indicates that an entity is associated with one or more defined census areas used for demographic or statistical purposes.
-
B.
hasCensusFunction
Indicates that an entity holds an official role or responsibility related to conducting, managing, or supporting census activities.
-
C.
hasMunicipalAreaFeature
Indicates that a municipal area possesses or contains a specific geographic or infrastructural feature within its boundaries.
-
D.
hasMunicipalUnitArea
Indicates that a municipal unit is associated with a specific measured area.
-
E.
hasCivilArea
Indicates that an administrative or political entity encompasses or is associated with a specific civil (local administrative) area.
- 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_69d8b9fab8a8819086a9ddc0871715e0 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e519537eb88190bc85d21471e27c26 |
completed | April 19, 2026, 6:05 p.m. |
| PD | Predicate disambiguation | batch_69e469bf7f74819096a01173493412c2 |
completed | April 19, 2026, 5:35 a.m. |
Created at: April 10, 2026, 10:46 a.m.