Triple
T17917529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Churcampa Province |
E447970
|
entity |
| Predicate | hasTransportPattern |
P87177
|
FINISHED |
| Object | rural road network |
—
|
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: rural road network | Statement: [Churcampa Province, hasTransportPattern, rural road network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTransportPattern Context triple: [Churcampa Province, hasTransportPattern, rural road network]
-
A.
hasTransportRoute
Indicates that there exists a designated transportation connection or route linking one entity to another.
-
B.
hasTransportationElement
chosen
Indicates that one entity includes, involves, or is associated with a specific transportation-related component or feature.
-
C.
hasTransportCategory
Indicates that one entity is classified under a particular category or type of transport associated with another entity.
-
D.
hasTransportCode
Indicates that an entity is associated with a specific transport-related code used to identify or classify its mode, method, or details of transportation.
-
E.
hasCommuterPattern
Indicates that there is a characteristic or recurring pattern in how an entity regularly travels between locations, typically for work or daily activities.
- 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a30778fc81908b5b2e308fb158a5 |
completed | April 19, 2026, 9:40 a.m. |
| PD | Predicate disambiguation | batch_69e3d8ec2f6881909d7f54b878cbed37 |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:20 a.m.