Triple
T36610704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caucasian Tats |
E903456
|
entity |
| Predicate | historicalLanguageInfluenceFrom |
P55689
|
FINISHED |
| Object | Persian language |
—
|
NE NERFINISHED |
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: Persian language | Statement: [Caucasian Tats, historicalLanguageInfluenceFrom, Persian language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: historicalLanguageInfluenceFrom Context triple: [Caucasian Tats, historicalLanguageInfluenceFrom, Persian language]
-
A.
historicalLanguageInfluenceOn
chosen
Indicates that one language has had a shaping or contributory effect on the development, vocabulary, structure, or usage of another language over time.
-
B.
languageInfluence
Indicates that one language has an effect on the development, usage, or characteristics of another language.
-
C.
introducedLanguageInfluenceTo
Indicates that one entity initiated or brought a particular linguistic influence or feature into another entity or context.
-
D.
historicalLanguage
Indicates that one language is a historical or earlier form/ancestor of another language.
-
E.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
- 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_69f76e6960e4819092047756ceb9a17e |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a013f09b0988190ba3179c7d56c726a |
completed | May 11, 2026, 2:29 a.m. |
| PD | Predicate disambiguation | batch_6a013e600e248190a1a9c363702c8586 |
completed | May 11, 2026, 2:26 a.m. |
Created at: May 3, 2026, 4:11 p.m.