Triple
T29130638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Orthez |
E738364
|
entity |
| Predicate | capturedFrench |
P14905
|
FINISHED |
| Object | about 6,000 prisoners |
—
|
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 6,000 prisoners | Statement: [Battle of Orthez, capturedFrench, about 6,000 prisoners]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: capturedFrench Context triple: [Battle of Orthez, capturedFrench, about 6,000 prisoners]
-
A.
FrenchObjective
Indicates that an entity serves as the goal, target, or object of an action or relation specifically within a French linguistic or contextual framework.
-
B.
limitedFrenchArmyTo
Indicates that an entity imposed restrictions or constraints on the size, capabilities, or actions of the French army relative to another entity or condition.
-
C.
FrenchCommander
Indicates that an entity serves as a military commander associated with France.
-
D.
FrenchCasualties
chosen
Indicates that the relationship specifies the number or extent of casualties suffered by French forces in a given event or context.
-
E.
involvedFrenchTroops
Indicates that the event, action, or situation included the participation or presence of French military forces.
- 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_69f07cb29cdc8190afa55444553de60c |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f66d7765208190b87b1cc6d96a151c |
completed | May 2, 2026, 9:32 p.m. |
| PD | Predicate disambiguation | batch_69f66abfdaf08190a55f14c70be6fd4d |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 28, 2026, 11:31 a.m.