Triple
T3512117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leopold Joseph von Daun |
E74219
|
entity |
| Predicate | strategyCharacteristic |
P662
|
FINISHED |
| Object | cautious |
—
|
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: cautious | Statement: [Leopold Joseph von Daun, strategyCharacteristic, cautious]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: strategyCharacteristic Context triple: [Leopold Joseph von Daun, strategyCharacteristic, cautious]
-
A.
policyCharacteristic
Indicates that a policy possesses a particular attribute, feature, or quality that characterizes how it is defined or operates.
-
B.
characterizedBy
chosen
Indicates that one entity possesses a defining quality, feature, or attribute expressed by another entity.
-
C.
strategyEmphasis
Indicates an entity’s primary focus or priority within a broader strategy or plan.
-
D.
trainingCharacteristic
Indicates that an entity has a specific property, feature, or quality related to training (such as method, intensity, or style).
-
E.
pricingCharacteristic
Indicates how the price of something is determined, structured, or behaves (e.g., fixed, variable, discounted, tiered).
- 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_69ad85cfb5c881909c9a2edd9d6043cc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc10b6b48190bedfed6d34afc425 |
completed | March 8, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69adae0e770481908528fa35eda53003 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:19 p.m.