Triple
T32388655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baney District |
E827605
|
entity |
| Predicate | climateCountryGeneral |
P193
|
FINISHED |
| Object | tropical |
—
|
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: tropical | Statement: [Baney District, climateCountryGeneral, tropical]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: climateCountryGeneral Context triple: [Baney District, climateCountryGeneral, tropical]
-
A.
climateBetween
Indicates that something (such as a location, period, or condition) has a climate that lies within a specified range or intermediate state between two other climatic conditions.
-
B.
hasClimate
chosen
Indicates that an entity possesses or is characterized by a particular type of climate or climatic conditions.
-
C.
country2
Indicates a secondary or alternative country associated with an entity, such as a second nationality, location, or jurisdiction.
-
D.
countryExample
Indicates that one country serves as a representative or illustrative example of another country in a given context.
-
E.
countryType
Indicates the classification or category of a country based on a specified typology (e.g., political, economic, or geographic type).
- 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_69f349184e7481909c6c54428cb9cf12 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c1d3dea88190985d0faef0c85551 |
completed | May 3, 2026, 3:32 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6eb32c8190bf405b2011fa48f7 |
completed | May 3, 2026, 3:01 a.m. |
Created at: May 1, 2026, 12:52 a.m.