Triple
T23500511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carol Anne Freeling |
E571824
|
entity |
| Predicate | abductedThrough |
P152621
|
FINISHED |
| Object | television set |
—
|
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: television set | Statement: [Carol Anne Freeling, abductedThrough, television set]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: abductedThrough Context triple: [Carol Anne Freeling, abductedThrough, television set]
-
A.
abductedBy
Indicates that an entity has been forcibly taken or carried away by another entity against their will.
-
B.
abductedFrom
Indicates that an entity was forcibly taken away or kidnapped from a specified location or source.
-
C.
countryOfAbduction
Indicates the country in which the abduction of the referenced entity took place.
-
D.
wasKidnapped
Indicates that an entity was forcibly taken and held against their will by another entity.
-
E.
usedAbductions
Indicates that one entity carried out or relied on abductions (kidnappings) as a method or tactic in relation to another entity or context.
- 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_69e245b4829881909b77a70e942bbd54 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a8fc37908190af86a01ab85737d6 |
completed | April 29, 2026, 6:45 a.m. |
| PD | Predicate disambiguation | batch_69f0621165c08190a0b27b1319733959 |
completed | April 28, 2026, 7:30 a.m. |
| PDg | Predicate description generation | batch_69f0bd4a0e408190ad8916faf23562d9 |
completed | April 28, 2026, 1:59 p.m. |
Created at: April 17, 2026, 6:06 p.m.