Triple
T15024848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara Stone |
E378182
|
entity |
| Predicate | relationshipToKidnappers |
P101216
|
FINISHED |
| Object | hostage |
—
|
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: hostage | Statement: [Barbara Stone, relationshipToKidnappers, hostage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToKidnappers Context triple: [Barbara Stone, relationshipToKidnappers, hostage]
-
A.
hasRelationshipToPerpetrator
chosen
Indicates that an entity has a specified type of relationship or connection to the perpetrator of an act or event.
-
B.
victimRelation
Indicates that one entity is the victim or target of harm, wrongdoing, or an adverse action caused by another entity.
-
C.
relationshipToTramp
Indicates that one entity has a specified type of relationship or connection to a tramp (a vagrant or homeless person).
-
D.
oftenKidnaps
Indicates that one entity frequently or habitually kidnaps another entity.
-
E.
relationshipToChildren
Indicates the type of familial or social connection an entity has to one or more children.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7de117c8190a1b9fa8d1602057e |
completed | April 15, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69de9a67cbc481909c19c2de57de4eb7 |
completed | April 14, 2026, 7:50 p.m. |
Created at: April 10, 2026, 2:56 a.m.