Triple
T25078679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taiwan provincial highway system |
E628121
|
entity |
| Predicate | includesUrbanSectionsIn |
P147867
|
FINISHED |
| Object | Taipei |
—
|
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: Taipei | Statement: [Taiwan provincial highway system, includesUrbanSectionsIn, Taipei]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesUrbanSectionsIn Context triple: [Taiwan provincial highway system, includesUrbanSectionsIn, Taipei]
-
A.
hasUrbanSectionsIn
chosen
Indicates that an entity includes or contains sections that are classified as urban within a specified area or region.
-
B.
isUrbanSectionOf
Indicates that one area or segment is the part of a larger entity that lies within an urban or city environment.
-
C.
includesUrbanApproach
Indicates that something incorporates or accounts for an urban-focused method, perspective, or component within its overall approach.
-
D.
containsUrbanForm
Indicates that one entity spatially includes or encompasses an urban form or built-up area within its extent.
-
E.
hasUrbanUnits
Indicates that an entity possesses or includes one or more urban units (such as cities, towns, or urbanized areas) within its scope or structure.
- 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_69e2ff2e73f881909992bf3eda5c25cb |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f69383222c81909d8baa04129d5c81 |
completed | May 3, 2026, 12:14 a.m. |
| PD | Predicate disambiguation | batch_69f690eb1e948190aab41a89969519a5 |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 18, 2026, 6:21 a.m.