Triple
T764006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaiju |
E16133
|
entity |
| Predicate | commonTheme |
P6627
|
FINISHED |
| Object | destruction of cities |
—
|
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: destruction of cities | Statement: [Kaiju, commonTheme, destruction of cities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonTheme Context triple: [Kaiju, commonTheme, destruction of cities]
-
A.
themeExamples
Indicates that the related entity serves as an example or illustration of the theme expressed by the subject.
-
B.
theme
Indicates the entity that is the primary participant or content affected or characterized by an action, event, or state.
-
C.
commonIn
Indicates that something frequently occurs, appears, or is found within a specified context, group, or environment.
-
D.
themeContrast
Indicates a relationship where two themes are compared or opposed to highlight their differences or tension.
-
E.
hasCentralTheme
chosen
Indicates that one entity serves as the primary or dominant theme or subject matter of another entity.
- 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_69a493684ee48190bd43b7c78da4aec8 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a69c8c448190a036a04fd8fdd2c2 |
completed | March 1, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69a4a506106081909ef97a679ff00a5a |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.