Triple
T31177274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liévin |
E794784
|
entity |
| Predicate | hasUrbanUnit |
P156670
|
FINISHED |
| Object | Lens–Liévin |
—
|
NE NERFINISHED |
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: Lens–Liévin | Statement: [Liévin, hasUrbanUnit, Lens–Liévin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUrbanUnit Context triple: [Liévin, hasUrbanUnit, Lens–Liévin]
-
A.
hasUrbanUnits
chosen
Indicates that an entity possesses or includes one or more urban units (such as cities, towns, or urbanized areas) within its scope or structure.
-
B.
hasUrbanSectionsIn
Indicates that an entity includes or contains sections that are classified as urban within a specified area or region.
-
C.
hasUrbanFunction
Indicates that an entity serves a specific role or purpose within an urban context, such as providing services, infrastructure, or activities typical of a city environment.
-
D.
hasUrbanDistrictFunction
Indicates that an entity serves the administrative or functional role of an urban district within a larger territorial or governance structure.
-
E.
hasUrbanFeature
Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
- 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_69f224d5b9708190b6ca79ad2fd3a28a |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6a916d2e08190bafc01cba73b6469 |
completed | May 3, 2026, 1:47 a.m. |
| PD | Predicate disambiguation | batch_69f6a7548eb48190a69b60a3c6ad53b9 |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 29, 2026, 9:08 p.m.