Triple
T37667454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Action of 23 November 1939 |
E937858
|
entity |
| Predicate | approximateBritishCasualties |
P39453
|
FINISHED |
| Object | over 200 killed |
—
|
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 200 killed | Statement: [Action of 23 November 1939, approximateBritishCasualties, over 200 killed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateBritishCasualties Context triple: [Action of 23 November 1939, approximateBritishCasualties, over 200 killed]
-
A.
englishCasualtiesKilledAndWounded
Indicates the number of English individuals who were either killed or wounded as a result of a particular event or conflict.
-
B.
casualties_British_side
chosen
Indicates the number or extent of casualties suffered by the British side in a conflict or incident.
-
C.
militaryCasualtiesEstimate
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
-
D.
casualtiesBritishWounded
Indicates the number of British individuals who were wounded as a result of a specific event or action.
-
E.
casualtiesUnion
Indicates a relationship where multiple casualty figures or reports are combined into a single aggregated total.
- 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_69f76ed6df7c8190b018e5baea716ceb |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ffc605b0648190a7abe9128b0d857a |
completed | May 9, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69ffc5742d80819099f947ece78d5700 |
completed | May 9, 2026, 11:38 p.m. |
Created at: May 3, 2026, 4:18 p.m.