Triple
T26701134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gene Hackman as Little Bill Daggett |
E673157
|
entity |
| Predicate | notableVictimOfViolence |
P870
|
FINISHED |
| Object | English Bob |
—
|
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: English Bob | Statement: [Gene Hackman as Little Bill Daggett, notableVictimOfViolence, English Bob]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableVictimOfViolence Context triple: [Gene Hackman as Little Bill Daggett, notableVictimOfViolence, English Bob]
-
A.
notableVictim
chosen
Indicates that the subject is a person or entity who is notably recognized as a victim of the object (such as an event, crime, or harmful action).
-
B.
notableVictims
Indicates that the object is a person or group who is especially well-known or significant as a victim of the subject.
-
C.
victimsOfCrime
Indicates that the subject has suffered harm or loss as a result of a criminal act committed by the object.
-
D.
notableViolentCase
Indicates that an entity is associated with a specific case or incident that is recognized as involving significant or noteworthy violence.
-
E.
honorsVictimOf
Indicates that one entity pays tribute or respect to another entity who has suffered harm, loss, or injustice.
- 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_69eecda2b49c8190a6c481cfc4c07954 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69ff0e9c75208190a4423261f00b79b3 |
completed | May 9, 2026, 10:38 a.m. |
| PD | Predicate disambiguation | batch_69ff0e07f08481909c4ae322632a6bf0 |
completed | May 9, 2026, 10:35 a.m. |
Created at: April 27, 2026, 3:31 a.m.