Triple
T1406090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Filter |
E31694
|
entity |
| Predicate | hasScenario |
P19962
|
FINISHED |
| Object | filter mostly behind humanity |
—
|
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: filter mostly behind humanity | Statement: [Great Filter, hasScenario, filter mostly behind humanity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScenario Context triple: [Great Filter, hasScenario, filter mostly behind humanity]
-
A.
hasStep
Indicates that one entity includes, is composed of, or is associated with a specific step or stage in a process involving another entity.
-
B.
hasScope
Indicates that one entity defines, limits, or encompasses the range, extent, or applicability within which another entity operates or is valid.
-
C.
hasCase
Indicates that one entity is involved in, associated with, or characterized by a particular case, instance, or occurrence represented by another entity.
-
D.
hasUseCase
chosen
Indicates that one entity is employed, applied, or utilized as a solution or method to address a particular need, problem, or scenario associated with another entity.
-
E.
hasPar
Indicates a relationship where one entity has another entity as its parent.
- 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_69a49918e1f88190ba610f9dc8114578 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3bc55a08190a4dfe13a5378aff3 |
completed | March 1, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69a4bf030a388190bc82d30b9233e873 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.