Triple
T23993213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FIRST National Advisor |
E605120
|
entity |
| Predicate | decisionInfluenceArea |
P128188
|
FINISHED |
| Object | program development |
—
|
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: program development | Statement: [FIRST National Advisor, decisionInfluenceArea, program development]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: decisionInfluenceArea Context triple: [FIRST National Advisor, decisionInfluenceArea, program development]
-
A.
decisionMakingArea
chosen
Indicates the area or domain within which a decision is made or for which a decision-making process is responsible.
-
B.
influencedPolicyArea
Indicates that one entity has affected, shaped, or guided the development, direction, or implementation of a particular policy area associated with another entity.
-
C.
rulesInfluence
Indicates that one set of rules or regulations has an effect on, shapes, or constrains another entity, process, or outcome.
-
D.
influencesRegion
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
-
E.
typeOfInfluence
Indicates the specific nature or category of influence that one entity exerts on another.
- 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_69e295463f7c8190b1c19dbd114641b9 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d38ce7fc8190a488991b6f61416b |
completed | April 29, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69f1615994c48190a5de95d3f7e5cd0a |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:37 p.m.