Triple
T29395685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nohkalikai Falls |
E745492
|
entity |
| Predicate | nearbyWeatherCharacteristic |
P85695
|
FINISHED |
| Object | very high annual rainfall |
—
|
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: very high annual rainfall | Statement: [Nohkalikai Falls, nearbyWeatherCharacteristic, very high annual rainfall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyWeatherCharacteristic Context triple: [Nohkalikai Falls, nearbyWeatherCharacteristic, very high annual rainfall]
-
A.
nearbyRegionCharacterizedBy
chosen
Indicates that a region located nearby another entity is defined or distinguished by a particular characteristic, feature, or condition.
-
B.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
-
C.
nearbyCurrent
Indicates that one entity is located close to another entity at the present moment or in the current context.
-
D.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
E.
nearbyFeature
Indicates that one entity is located close to or in the immediate vicinity of 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_69f0a79dfabc81908755382ee47791e2 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69fcef654d588190b29ecc76678d1aa0 |
completed | May 7, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69fcecdb97f48190b382b7d13be92dc0 |
completed | May 7, 2026, 7:49 p.m. |
Created at: April 28, 2026, 2:46 p.m.