Triple
T7330217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Carpet Challenge |
E168978
|
entity |
| Predicate | typeOfImpact |
P51728
|
FINISHED |
| Object | awareness-raising |
—
|
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: awareness-raising | Statement: [Green Carpet Challenge, typeOfImpact, awareness-raising]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfImpact Context triple: [Green Carpet Challenge, typeOfImpact, awareness-raising]
-
A.
impactCategory
chosen
Indicates the type or domain of effect that one entity or action has on another, classifying the nature of its impact.
-
B.
recognizesImpactOn
Indicates that one entity acknowledges or understands the effect or consequences it has on another entity or situation.
-
C.
indirectImpactOn
Indicates that one entity affects another entity’s state, condition, or outcome through one or more intermediate factors rather than through a direct interaction.
-
D.
timeHorizonOfImpact
Indicates the span of time over which an action, event, or factor is expected to produce its effects or consequences.
-
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_69c68a568a6481908f11e20db7bc8446 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f0ab0b1881909f8f086b81fdddb7 |
completed | March 27, 2026, 9:03 p.m. |
| PD | Predicate disambiguation | batch_69c6e77230048190b2c29ca6b3a65b8e |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 3:03 p.m.