Triple
T5176314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Helsingborg |
E116806
|
entity |
| Predicate | approximateSwedishStrength |
P2981
|
FINISHED |
| Object | about 14,000–15,000 men |
—
|
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: about 14,000–15,000 men | Statement: [Battle of Helsingborg, approximateSwedishStrength, about 14,000–15,000 men]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateSwedishStrength Context triple: [Battle of Helsingborg, approximateSwedishStrength, about 14,000–15,000 men]
-
A.
strengthSweden
Indicates the level or degree of strength associated with Sweden in a given context.
-
B.
estimatedStrength
chosen
Indicates that a value represents an approximate or inferred level, magnitude, or intensity of something rather than a precisely measured strength.
-
C.
approximateStrengthFrancoBavarian
Indicates an estimated or inferred degree of strength or intensity in the Franco-Bavarian relationship or interaction.
-
D.
nameInSwedish
Indicates that an entity is known or referred to by a particular name when expressed in the Swedish language.
-
E.
strengthDenmark
Indicates a measure or characterization of Denmark’s power, influence, or robustness in a given context.
- 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_69bd446140f08190becb93c61158f27f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd797349008190b87ad9d0d3eb667f |
completed | March 20, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69bd77b529948190b86671ebe43f4734 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:45 p.m.