Triple
T2502034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Óscar Romero |
E52485
|
entity |
| Predicate | killedWhile |
P11793
|
FINISHED |
| Object | celebrating Mass |
—
|
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: celebrating Mass | Statement: [Óscar Romero, killedWhile, celebrating Mass]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: killedWhile Context triple: [Óscar Romero, killedWhile, celebrating Mass]
-
A.
killedDuring
Indicates that one entity caused the death of another entity in the course of, or as part of, a specified event or time period.
-
B.
killedBy
Indicates that one entity caused the death of another entity.
-
C.
killedAt
Indicates that a killing event occurred at a specific location or time associated with the entities involved.
-
D.
diedWhile
chosen
Indicates that one entity ceased to live during the occurrence or performance of another specified event or activity.
-
E.
killedApproximate
Indicates that one entity caused the death of another, but the information about this killing is uncertain, estimated, or not known with exact precision.
- 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1c9f80c8190b40ada396e184e75 |
completed | March 7, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69abd0bba5348190bb4637d3165cb339 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.