Triple
T5846822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southeastern Iranian languages |
E129731
|
entity |
| Predicate | hasNotableLanguage |
P7390
|
FINISHED |
| Object | Wanetsi |
E46312
|
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: Wanetsi | Statement: [Southeastern Iranian languages, hasNotableLanguage, Wanetsi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wanetsi Context triple: [Southeastern Iranian languages, hasNotableLanguage, Wanetsi]
-
A.
Wanetsi
chosen
Wanetsi is a distinct and archaic variety of Pashto spoken by a small community in parts of Afghanistan and Pakistan.
-
B.
Oshikwambi
Oshikwambi is a regional dialect of the Oshiwambo language spoken by the Kwambi people in northern Namibia.
-
C.
Nansio
Nansio is the main town and administrative center of Ukerewe Island in Lake Victoria, Tanzania.
-
D.
Ongwediva
Ongwediva is a growing town in northern Namibia known as an educational and commercial hub, hosting institutions like the University of Namibia’s campus and the annual Ongwediva Trade Fair.
-
E.
Wanze
Wanze is a municipality in eastern Belgium situated along the Meuse River in the Walloon Region.
- 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_69c0084bd31c8190a796bb6284845e83 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0351157508190a78d2a7141e0cee8 |
completed | March 22, 2026, 6:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a1ad4d888190b4a1e605887b2e2c |
completed | March 23, 2026, 2:13 a.m. |
Created at: March 22, 2026, 3:55 p.m.