Triple
T34253590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 93rd Division (Provisional) |
E878810
|
entity |
| Predicate | hasLanguageOfOperationalOrders |
P39447
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [93rd Division (Provisional), hasLanguageOfOperationalOrders, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOfOperationalOrders Context triple: [93rd Division (Provisional), hasLanguageOfOperationalOrders, French]
-
A.
hasLanguageOfOrders
chosen
Indicates that one entity uses or is associated with a particular language for issuing orders or commands to another entity.
-
B.
hasPrimaryLanguageOfOperations
Indicates that an entity conducts its main activities or operations primarily using a specified language.
-
C.
hasLanguageOfMission
Indicates that an entity (such as a mission or project) is associated with a specific language used for its communication, documentation, or operation.
-
D.
hasOfficerLanguage
Indicates that an officer is able or authorized to communicate in a specified language.
-
E.
languageOfOperation
Indicates the language in which an entity (such as a system, service, or process) primarily operates or functions.
- 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_69f349b421cc8190b4b4655e1d612548 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff21cbd9108190a52c0ba42004c669 |
completed | May 9, 2026, noon |
| PD | Predicate disambiguation | batch_69ff1faea91881908c626c70bca5100a |
completed | May 9, 2026, 11:51 a.m. |
Created at: May 1, 2026, 1:56 a.m.