Triple
T38574210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penang State Forestry Department |
E929356
|
entity |
| Predicate | sectoralPolicyArea |
P97586
|
FINISHED |
| Object | environmental protection in Penang |
—
|
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: environmental protection in Penang | Statement: [Penang State Forestry Department, sectoralPolicyArea, environmental protection in Penang]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sectoralPolicyArea Context triple: [Penang State Forestry Department, sectoralPolicyArea, environmental protection in Penang]
-
A.
economicPolicyArea
Indicates the specific domain or sector of economic policy to which an action, measure, or issue is related.
-
B.
commonPolicyArea
Indicates that two entities share the same policy domain, topic, or area of regulatory or legislative focus.
-
C.
sectoralEngagement
Indicates engagement or involvement between entities within a specific sector or industry context.
-
D.
sectoralCoverage
chosen
Indicates the specific sectors, industries, or domains to which something (such as a policy, agreement, or dataset) applies or extends.
-
E.
sectoralExample
Indicates that something serves as a representative or illustrative example within a particular sector or industry context.
- 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_69f76ebd2248819083978362d81fa35e |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcdaa36f90819093f8661969990c7d |
completed | May 7, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69fcd8fefc588190b063d7ea1ec87b07 |
completed | May 7, 2026, 6:25 p.m. |
Created at: May 3, 2026, 4:32 p.m.