Triple
T12928002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arcadia Oaks |
E309295
|
entity |
| Predicate | hasStoryTheme |
P76865
|
FINISHED |
| Object | coexistence of magical and ordinary worlds |
—
|
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: coexistence of magical and ordinary worlds | Statement: [Arcadia Oaks, hasStoryTheme, coexistence of magical and ordinary worlds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStoryTheme Context triple: [Arcadia Oaks, hasStoryTheme, coexistence of magical and ordinary worlds]
-
A.
hasThemeInStory
chosen
Indicates that a particular theme is present or plays a significant role within a given story.
-
B.
hasSayingTheme
Indicates that a saying, proverb, or quoted expression is about or centers on a particular theme or subject.
-
C.
hasCentralTheme
Indicates that one entity serves as the primary or dominant theme or subject matter of another entity.
-
D.
fictionalTheme
Indicates that a work, element, or context is centered around or characterized by a fictional theme or motif.
-
E.
hasThematicOrigin
Indicates that something originates from, or is thematically derived from, a particular source, subject, or theme.
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971ec72a48190aceef10630603d2c |
completed | April 10, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69d96fab4d0881909a7a4d66bab9aa85 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:42 p.m.