Triple
T28152424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Makalu region |
E714653
|
entity |
| Predicate | relativeRemoteness |
P3560
|
FINISHED |
| Object | remote |
—
|
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: remote | Statement: [Mount Makalu region, relativeRemoteness, remote]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativeRemoteness Context triple: [Mount Makalu region, relativeRemoteness, remote]
-
A.
remoteness
chosen
Indicates the degree of physical or conceptual distance or isolation between entities.
-
B.
featuresRegionalProximity
Indicates that one entity is located near or in close geographic proximity to a particular region or another entity.
-
C.
hasNearbySettlementDensity
Indicates that an entity is associated with a concentration of settlements located within a nearby surrounding area.
-
D.
isRemoteFromMajorTowns
Indicates that something is located far away from major towns or urban centers, with limited proximity to them.
-
E.
hasUrbanProximity
Indicates that one entity is located near or within easy access to an urban area associated with another entity.
- 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_69efd6b033208190bf74f80a147e2092 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69ffcb5536d88190bfc2e00b854cacfb |
completed | May 10, 2026, 12:03 a.m. |
| PD | Predicate disambiguation | batch_69ffc900c2a081909dea04aa60566923 |
completed | May 9, 2026, 11:53 p.m. |
Created at: April 27, 2026, 10 p.m.