Triple
T697470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reginald Dyer |
E13922
|
entity |
| Predicate | estimatedNumberOfCasualtiesByOtherSources |
P661
|
FINISHED |
| Object | over 1000 |
—
|
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: over 1000 | Statement: [Reginald Dyer, estimatedNumberOfCasualtiesByOtherSources, over 1000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedNumberOfCasualtiesByOtherSources Context triple: [Reginald Dyer, estimatedNumberOfCasualtiesByOtherSources, over 1000]
-
A.
casualtiesEstimate
chosen
Indicates an estimated number of people killed, injured, or otherwise harmed as a result of an event or incident.
-
B.
militaryCasualtiesEstimate
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
-
C.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
D.
casualtiesDescription
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
-
E.
casualtiesImpact
Indicates how the number or severity of casualties affects or influences another factor, situation, or outcome.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0c8055881909565ebde2be8fd7a |
completed | March 1, 2026, 8:25 p.m. |
| PD | Predicate disambiguation | batch_69a49d2586b081908e052cc5ba1d2685 |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.