Triple
T9698334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3rd Special Forces Group (Airborne) |
E234709
|
entity |
| Predicate | languageSkillFocus |
P45342
|
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: [3rd Special Forces Group (Airborne), languageSkillFocus, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageSkillFocus Context triple: [3rd Special Forces Group (Airborne), languageSkillFocus, French]
-
A.
languageCapacity
Indicates the extent to which an entity is able to understand, produce, or otherwise use language.
-
B.
focusesOnLanguage
Indicates that an entity’s primary attention, activity, or content is directed toward language as its main subject or concern.
-
C.
languageModality
Indicates the mode or form in which a language is expressed or perceived (e.g., spoken, signed, written, or tactile).
-
D.
languageProvision
Indicates that one entity supplies, supports, or makes available a particular language (or set of languages) for use by another entity.
-
E.
skillEmphasis
chosen
Indicates that a particular skill is given special focus, priority, or importance within a context such as a role, task, or curriculum.
- 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_69ca84cb580c8190a7e5f4b3bcdaf2a4 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d3c02e0819098d05c68805689f1 |
completed | April 1, 2026, 10:33 p.m. |
| PD | Predicate disambiguation | batch_69cd03b641408190942464eaf174c6b5 |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:18 p.m.