Triple
T1843558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Training Wing 1 |
E41231
|
entity |
| Predicate | hasDesignationLanguage |
P26955
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Training Wing 1, hasDesignationLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDesignationLanguage Context triple: [Training Wing 1, hasDesignationLanguage, English]
-
A.
hasOfficerLanguage
Indicates that an officer is able or authorized to communicate in a specified language.
-
B.
isLanguageOf
Indicates that a particular language is used as the official or primary language associated with a given entity (such as a person, document, or region).
-
C.
languageDesigned
Indicates that one entity created or developed the language used or associated with another entity.
-
D.
hasLanguageOfOfficialName
chosen
Indicates that an entity’s official name is expressed in a specified language.
-
E.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
- 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_69a88648cd44819093303206d96d76ad |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb32d35508190bf1c487dffbecaf0 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafdb0d2c8190a67f584e67979fa3 |
completed | March 7, 2026, 4:55 a.m. |
Created at: March 4, 2026, 7:33 p.m.