Triple
T28689535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Nile Falls |
E729235
|
entity |
| Predicate | impactingFactor |
P103390
|
FINISHED |
| Object | upstream hydroelectric dams |
—
|
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: upstream hydroelectric dams | Statement: [Blue Nile Falls, impactingFactor, upstream hydroelectric dams]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactingFactor Context triple: [Blue Nile Falls, impactingFactor, upstream hydroelectric dams]
-
A.
impactCategory
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.
impactDescription
chosen
Indicates a description of the effect, consequence, or influence that one entity, action, or event has on another.
-
D.
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.
-
E.
impactStatus
Indicates the current state or condition of how something has affected or influenced a target.
- 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_69f043e60b6c8190ac2cd042e77fe6e9 |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f6c1265c208190aacd2b551f8f0f82 |
completed | May 3, 2026, 3:29 a.m. |
| PD | Predicate disambiguation | batch_69f6bd2415fc81908c23c311aebce66f |
completed | May 3, 2026, 3:12 a.m. |
Created at: April 28, 2026, 5:35 a.m.