Triple
T36196856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle between USS Constitution and HMS Java |
E1047150
|
entity |
| Predicate | casualtiesUnitedKingdomWounded |
P4808
|
FINISHED |
| Object | over 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 100 | Statement: [Battle between USS Constitution and HMS Java, casualtiesUnitedKingdomWounded, over 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesUnitedKingdomWounded Context triple: [Battle between USS Constitution and HMS Java, casualtiesUnitedKingdomWounded, over 100]
-
A.
casualtiesBritishWounded
chosen
Indicates the number of British individuals who were wounded as a result of a specific event or action.
-
B.
casualtiesUnionWounded
Indicates that the number of casualties specifically refers to Union forces who were wounded.
-
C.
englishCasualtiesKilledAndWounded
Indicates the number of English individuals who were either killed or wounded as a result of a particular event or conflict.
-
D.
casualtiesWounded
Indicates that an event or situation resulted in people being injured but not killed.
-
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_69f76e414bdc8190996f15a544220a3d |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c29e1b848190b945c6c6120a5330 |
completed | May 3, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b6e7a881908deb96bedb2713f4 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:08 p.m.