Triple
T19540326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vasusena |
E488880
|
entity |
| Predicate | killedUnderCircumstances |
P4707
|
FINISHED |
| Object | when unarmed and chariot-wheel was stuck |
—
|
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: when unarmed and chariot-wheel was stuck | Statement: [Vasusena, killedUnderCircumstances, when unarmed and chariot-wheel was stuck]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: killedUnderCircumstances Context triple: [Vasusena, killedUnderCircumstances, when unarmed and chariot-wheel was stuck]
-
A.
killedBecauseOf
Indicates that one entity killed another specifically due to a particular reason, motive, or triggering factor associated with that other entity or a related circumstance.
-
B.
killedBy
Indicates that one entity caused the death of another entity.
-
C.
killedDuring
chosen
Indicates that one entity caused the death of another entity in the course of, or as part of, a specified event or time period.
-
D.
killedNear
Indicates that one entity killed another in close spatial proximity to a specified location or reference point.
-
E.
killedOnBehalfOf
Indicates that one entity carried out a killing as a representative of, or in service to, another entity.
- 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63871d00881909ed7371ae5577957 |
completed | April 20, 2026, 2:30 p.m. |
| PD | Predicate disambiguation | batch_69e514c9c00481909b76bda67957e58b |
completed | April 19, 2026, 5:45 p.m. |
Created at: April 10, 2026, 1:41 p.m.