Triple
T37368004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nathan Appleby |
E927762
|
entity |
| Predicate | hasThemeInStoryArc |
P76865
|
FINISHED |
| Object | conflict between science and superstition |
—
|
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: conflict between science and superstition | Statement: [Nathan Appleby, hasThemeInStoryArc, conflict between science and superstition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasThemeInStoryArc Context triple: [Nathan Appleby, hasThemeInStoryArc, conflict between science and superstition]
-
A.
hasThemeInStory
chosen
Indicates that a particular theme is present or plays a significant role within a given story.
-
B.
hasThemeInShow
Indicates that a particular theme is featured or present within a specific show.
-
C.
hasMainThemeCharacter
Indicates that a work (such as a story, film, or game) features a specific character as its central or primary thematic focus.
-
D.
hasThemeRelationship
Indicates a relationship where one entity is thematically related to, or centered around, another entity as its main subject or topic.
-
E.
hasCentralTheme
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_69f76eb820248190a5c395ca50ad002a |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbc36ce1f88190a7fa1656b714e107 |
completed | May 6, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69fbbd13595c81908719f52c3d37a7e8 |
completed | May 6, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:16 p.m.