Triple
T532596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reginald Dyer |
E12254
|
entity |
| Predicate | numberOfRoundsFired |
P6156
|
FINISHED |
| Object | approximately 1650 |
—
|
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: approximately 1650 | Statement: [Reginald Dyer, numberOfRoundsFired, approximately 1650]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRoundsFired Context triple: [Reginald Dyer, numberOfRoundsFired, approximately 1650]
-
A.
roundsFiredEstimate
chosen
Indicates an estimated number of shots or rounds that have been fired in a given context or event.
-
B.
numberLaunchedInCombat
Indicates the quantity of times an entity has been launched or deployed specifically in combat operations.
-
C.
roundCount
Indicates the number of discrete rounds or iterations that have occurred or are allocated within a process, event, or interaction.
-
D.
firstShotsFiredBy
Indicates which party or entity initiated a conflict or incident by discharging the first shots.
-
E.
numberOfTargets
Indicates the quantity of target entities associated with or affected by a given subject or event.
- 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_69a4933208e88190891f5debab1b776d |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4985e51908190a34aa82ea9dbee1e |
completed | March 1, 2026, 7:49 p.m. |
| PD | Predicate disambiguation | batch_69a494b3e49081909810fa417b31306f |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:32 p.m.