Triple
T1528571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kardashev scale |
E32388
|
entity |
| Predicate | hasApproximateHumanLevel |
P11340
|
FINISHED |
| Object | Type 0.x civilization |
—
|
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: Type 0.x civilization | Statement: [Kardashev scale, hasApproximateHumanLevel, Type 0.x civilization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateHumanLevel Context triple: [Kardashev scale, hasApproximateHumanLevel, Type 0.x civilization]
-
A.
intelligenceLevel
chosen
Indicates the degree or measure of cognitive ability or intelligence attributed to an entity.
-
B.
hasReasoningComplexity
Indicates that an action, process, or decision involves a certain level or type of cognitive or logical complexity in its reasoning.
-
C.
hasCognitiveComponent
Indicates that the related entity or process involves or depends on mental activities such as thinking, reasoning, perception, or understanding.
-
D.
automationLevel
Indicates the degree to which a process, task, or system is performed automatically rather than manually.
-
E.
hasLanguageModel
Indicates that an entity possesses, uses, or is associated with a particular language model.
- 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_69a885e9b0ac819093a9806ad0efc82c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a933ddc5a881909cdf503f2bc29bd4 |
completed | March 5, 2026, 7:42 a.m. |
| PD | Predicate disambiguation | batch_69a907ae8f688190ad9000ea1e018585 |
completed | March 5, 2026, 4:33 a.m. |
Created at: March 4, 2026, 7:26 p.m.