Triple
T34999037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | kidnapped Santa Claus |
E1009615
|
entity |
| Predicate | abductionOrderedBy |
P182195
|
FINISHED |
| Object | Jack Skellington |
—
|
NE NERFINISHED |
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: Jack Skellington | Statement: [kidnapped Santa Claus, abductionOrderedBy, Jack Skellington]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: abductionOrderedBy Context triple: [kidnapped Santa Claus, abductionOrderedBy, Jack Skellington]
-
A.
usedAbductions
Indicates that one entity carried out or relied on abductions (kidnappings) as a method or tactic in relation to another entity or context.
-
B.
abductionContext
Indicates a contextual relationship in which an abduction event occurs, specifying the surrounding circumstances, conditions, or setting of that abduction.
-
C.
abductionCause
Indicates a causal relationship where one entity is the reason or driving factor behind another entity’s abduction.
-
D.
abductedIn
Indicates that an entity was forcibly taken or kidnapped at or during a specified time or event.
-
E.
canAbduct
Indicates that one entity has the ability or potential to forcibly take away or kidnap another entity.
- F. None of above. chosen
Provenance (4 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_69f76dcb716881909f75e4fd60ab2284 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69f7870dfe108190996c0c68630edc7f |
completed | May 3, 2026, 5:34 p.m. |
Created at: May 3, 2026, 4:01 p.m.