Triple
T2528950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Major Erwin König |
E56107
|
entity |
| Predicate | fictionalRole |
P25662
|
FINISHED |
| Object | German sniper sent to hunt Vasily Zaitsev |
—
|
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: German sniper sent to hunt Vasily Zaitsev | Statement: [Major Erwin König, fictionalRole, German sniper sent to hunt Vasily Zaitsev]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalRole Context triple: [Major Erwin König, fictionalRole, German sniper sent to hunt Vasily Zaitsev]
-
A.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
B.
hasFictionalRole
chosen
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
C.
creativeRole
Indicates that an entity holds a specific creative function or responsibility in relation to another entity, such as a work or project.
-
D.
literaryRole
Indicates the specific narrative or functional role an entity holds within a literary work or text.
-
E.
featuresCharacterRole
Indicates that a work includes a character appearing in a specific narrative or functional role.
- 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_69ab4a48e4f081908f1218d244608659 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd25903f08190b46e12d32278daca |
completed | March 7, 2026, 7:23 a.m. |
| PD | Predicate disambiguation | batch_69abd0c2e34c8190a914d5c2afba147c |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.