Triple
T38480177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cawayan |
E915649
|
entity |
| Predicate | hasSectorDependency |
P202954
|
FINISHED |
| Object | marine resources |
—
|
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: marine resources | Statement: [Cawayan, hasSectorDependency, marine resources]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSectorDependency Context triple: [Cawayan, hasSectorDependency, marine resources]
-
A.
hasDependency
Indicates that one entity relies on or requires another entity in order to function, exist, or be fulfilled.
-
B.
hasSectorType
Indicates that an entity belongs to or is classified under a particular sector category or type.
-
C.
isSectorSpecific
Indicates that something is tailored or restricted to a particular industry or sector rather than being generally applicable.
-
D.
hasSectorization
Indicates that one entity is divided into, assigned to, or associated with specific sectors defined by another entity.
-
E.
belongsToSubsector
Indicates that one entity is part of, or classified within, a more specific subsector of a broader sector or industry.
- 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_69f76e8ff5cc8190a88803369183845e |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a00d508290081909f3d5dfbb2e80c8e |
completed | May 10, 2026, 6:57 p.m. |
| PD | Predicate disambiguation | batch_6a00d49da4cc81909566ad286ec22292 |
completed | May 10, 2026, 6:55 p.m. |
| PDg | Predicate description generation | batch_6a00d5073e188190b09916119a9c032d |
completed | May 10, 2026, 6:57 p.m. |
Created at: May 3, 2026, 4:31 p.m.