Triple
T1040135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tajiks |
E22451
|
entity |
| Predicate | closelyRelatedLanguage |
P10003
|
FINISHED |
| Object | Dari |
E109613
|
NE 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: Dari | Statement: [Tajiks, closelyRelatedLanguage, Dari]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dari Context triple: [Tajiks, closelyRelatedLanguage, Dari]
-
A.
Dari
chosen
Dari is a variety of the Persian language primarily spoken in Afghanistan and used in media, education, and government there.
-
B.
Dila
Dila is the commonly used short name for FC Dila Gori, a professional football club based in Gori, Georgia.
-
C.
Gulnare
Gulnare is a central female character in Lord Byron’s narrative poem "The Corsair," known for her courage, passion, and pivotal role in the story’s dramatic events.
-
D.
Nasar
Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
-
E.
Mardan
Mardan is a major city in northern Pakistan known as an important commercial and cultural center of the Khyber Pakhtunkhwa province.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a493d91478819094cc01fb65564bc1 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bb71e7f88190bf33bbe5ef2c68ff |
completed | March 1, 2026, 10:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac3bc58d8c8190b9dc7a4bc986abcb |
completed | March 7, 2026, 2:52 p.m. |
Created at: March 1, 2026, 7:41 p.m.