Triple
T33444855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Amateur |
E856464
|
entity |
| Predicate | hasFianceeCharacterKilledBy |
P116503
|
FINISHED |
| Object | terrorist attack |
—
|
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: terrorist attack | Statement: [The Amateur, hasFianceeCharacterKilledBy, terrorist attack]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFianceeCharacterKilledBy Context triple: [The Amateur, hasFianceeCharacterKilledBy, terrorist attack]
-
A.
fiancéeDied
Indicates that a person’s fiancée has died.
-
B.
spouseAssassinated
Indicates that one person's spouse was deliberately killed by another person.
-
C.
hasJealousHusbandCharacter
Indicates that an entity includes or involves a husband character who experiences or expresses jealousy in the context of the relationship or narrative.
-
D.
hasMurderVictimCharacter
chosen
Indicates that an entity (such as a work of fiction or event) includes or involves a character who is the victim of a murder.
-
E.
hasFianceeOfSonAsLover
Indicates that a person is in a romantic or sexual relationship with their son’s fiancée.
- 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_69f34971b75881908be360bb041f003c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff2636e2bc8190bba91eff91431c6e |
completed | May 9, 2026, 12:19 p.m. |
| PD | Predicate disambiguation | batch_69ff25c65be48190868480d94e1c4e89 |
completed | May 9, 2026, 12:17 p.m. |
Created at: May 1, 2026, 1:37 a.m.