Triple
T33411485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theudigisel |
E855593
|
entity |
| Predicate | killedAtEvent |
P4707
|
FINISHED |
| Object | banquet in Seville |
—
|
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: banquet in Seville | Statement: [Theudigisel, killedAtEvent, banquet in Seville]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: killedAtEvent Context triple: [Theudigisel, killedAtEvent, banquet in Seville]
-
A.
killedAfterEvent
Indicates that one entity killed another at a time occurring after a specified event.
-
B.
killedAt
Indicates that a killing event occurred at a specific location or time associated with the entities involved.
-
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.
killedBy
Indicates that one entity caused the death of another entity.
-
E.
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.
- 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_69f3496f04a08190804e56ac5098b8e4 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6e47f37848190aadb137c81760f1f |
completed | May 3, 2026, 6 a.m. |
| PD | Predicate disambiguation | batch_69f6e3da41948190a4cfe866ce184f73 |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:36 a.m.