Triple
T24540292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banggai Islands |
E607071
|
entity |
| Predicate | marineEcoregionStatus |
P156656
|
FINISHED |
| Object | high biodiversity value |
—
|
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: high biodiversity value | Statement: [Banggai Islands, marineEcoregionStatus, high biodiversity value]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marineEcoregionStatus Context triple: [Banggai Islands, marineEcoregionStatus, high biodiversity value]
-
A.
hasMarineEcoregion
Indicates that an entity is associated with, or located within, a specific marine ecoregion.
-
B.
marineAreaShare
Indicates the proportion of a larger marine area that is occupied, controlled, or otherwise attributed to a specific entity.
-
C.
hasMarineEcosystem
Indicates that an entity possesses, contains, or is associated with a marine ecosystem as part of its characteristics or environment.
-
D.
marineArea
Indicates a relationship where an entity is located in, associated with, or relevant to a specific marine or oceanic area.
-
E.
seaAreaDesignation
Indicates the specific legal or administrative sea area classification that has been assigned to a given marine region.
- 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_69e2c4c9bf94819082d05da6f5c29907 |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2be044d4c819094e14eda28d371a7 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6b0ca8081908d931aec560eae56 |
completed | April 30, 2026, 12:47 a.m. |
| PDg | Predicate description generation | batch_69f2b8b8bc5881908df49c0b07110246 |
completed | April 30, 2026, 2:04 a.m. |
Created at: April 18, 2026, 2:26 a.m.