Triple
T15292711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gunbalanya |
E365565
|
entity |
| Predicate | wetSeasonAccessibility |
P117988
|
FINISHED |
| Object | often accessible only by air |
—
|
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: often accessible only by air | Statement: [Gunbalanya, wetSeasonAccessibility, often accessible only by air]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wetSeasonAccessibility Context triple: [Gunbalanya, wetSeasonAccessibility, often accessible only by air]
-
A.
wetSeasonCapital
Indicates that a location serves as the capital or primary administrative center specifically during the wet season.
-
B.
hasSeasonalFlooding
Indicates that an area regularly experiences flooding during specific, recurring times of the year.
-
C.
primaryRainySeasonFor
Indicates that one entity is the main or most significant rainy season associated with a particular place or region.
-
D.
hasDrySeasonCause
Indicates that one factor or condition is the underlying cause of a location or region experiencing a dry season.
-
E.
accessibleYearRound
Indicates that the subject can be accessed or used during all seasons of the year without interruption.
- F. None of above. chosen
Provenance (4 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03682ea488190ac82fdbd0e855d34 |
completed | April 16, 2026, 1:08 a.m. |
| PD | Predicate disambiguation | batch_69deca935e2c8190b640987ddfc542b9 |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2e413481909d9180a8d78d2c17 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:15 a.m.