Triple
T29156610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makanrushi |
E739061
|
entity |
| Predicate | hasNoUrbanAreas |
P165806
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Makanrushi, hasNoUrbanAreas, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoUrbanAreas Context triple: [Makanrushi, hasNoUrbanAreas, true]
-
A.
hasNoUrbanSettlement
chosen
Indicates that the referenced area or region does not contain any urban settlements such as towns or cities.
-
B.
hasNoIncorporatedCities
Indicates that a geographic or administrative area contains no incorporated cities within its boundaries.
-
C.
hasRuralDistricts
Indicates that an entity is associated with, contains, or is administratively linked to one or more rural districts.
-
D.
isLessUrbanizedThan
Indicates that one place has a lower degree of urban development or urban characteristics compared to another place.
-
E.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic 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_69f07cb528fc8190a556b73990c347c8 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69ff7dcedab08190a719a707d03306e2 |
completed | May 9, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69ff7d0119348190ad462554e81190fe |
completed | May 9, 2026, 6:29 p.m. |
Created at: April 28, 2026, 11:45 a.m.