Triple
T34295341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of the Glorious First of June |
E880008
|
entity |
| Predicate | britishKilledAndWounded |
P39453
|
FINISHED |
| Object | over 1,100 |
—
|
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 1,100 | Statement: [Battle of the Glorious First of June, britishKilledAndWounded, over 1,100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: britishKilledAndWounded Context triple: [Battle of the Glorious First of June, britishKilledAndWounded, over 1,100]
-
A.
casualtiesBritishWounded
Indicates the number of British individuals who were wounded as a result of a specific event or action.
-
B.
englishCasualtiesKilledAndWounded
Indicates the number of English individuals who were either killed or wounded as a result of a particular event or conflict.
-
C.
casualties_British_side
chosen
Indicates the number or extent of casualties suffered by the British side in a conflict or incident.
-
D.
casualtiesUKKilled
Indicates that the relationship specifies the number of people from the UK who were killed in the referenced event or incident.
-
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_69f349b79f6c81909cb468c92c39c74d |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:57 a.m.