Triple
T20333337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waterless Flood |
E492538
|
entity |
| Predicate | effectOnWorld |
P139733
|
FINISHED |
| Object | near-extinction of human population |
—
|
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: near-extinction of human population | Statement: [Waterless Flood, effectOnWorld, near-extinction of human population]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnWorld Context triple: [Waterless Flood, effectOnWorld, near-extinction of human population]
-
A.
effectOnUser
Indicates how an action, event, or condition influences or impacts a user.
-
B.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
-
C.
effectOnLens
Indicates the influence or impact that one entity has on the properties, behavior, or performance of a lens.
-
D.
effectOnOthers
Indicates the impact or influence that one entity’s actions, presence, or state has on other entities.
-
E.
eventEffect
Indicates the resulting change, outcome, or consequence that one event has on another state, entity, or event.
- 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_69e0b4a1a09881908d97270d6971a25a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e677e94e2481908898e0a3513e1209 |
completed | April 20, 2026, 7 p.m. |
| PD | Predicate disambiguation | batch_69e5762655ac8190a8cc48a29fa2c0c4 |
completed | April 20, 2026, 12:41 a.m. |
| PDg | Predicate description generation | batch_69e58d7481508190a87c8b88f9df9879 |
completed | April 20, 2026, 2:20 a.m. |
Created at: April 16, 2026, 11:22 a.m.