Triple
T6700937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Castillon |
E152875
|
entity |
| Predicate | FrenchForceCharacteristic |
P7256
|
FINISHED |
| Object | entrenched artillery camp |
—
|
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: entrenched artillery camp | Statement: [Battle of Castillon, FrenchForceCharacteristic, entrenched artillery camp]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FrenchForceCharacteristic Context triple: [Battle of Castillon, FrenchForceCharacteristic, entrenched artillery camp]
-
A.
opponentForceCharacteristic
Indicates a characteristic or attribute that describes the nature, capability, or condition of an opposing force.
-
B.
describesForce
Indicates that one entity characterizes, explains, or specifies the nature or magnitude of a force acting on another entity or system.
-
C.
militaryCharacteristic
chosen
Indicates that one entity possesses a specific military-related attribute, quality, or feature in relation to another entity or context.
-
D.
FrenchCommander
Indicates that an entity serves as a military commander associated with France.
-
E.
FrenchUnit
Indicates that a unit or entity is associated with France, typically by origin, affiliation, or national identity.
- 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_69c68807adbc8190b8632df42b39eda0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16897e48190b43eda2206b14d6a |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d089c7488190a00853fb12f53b2a |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:05 p.m.