Triple
T15945085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kafa |
E386663
|
entity |
| Predicate | hasNeighboringLanguage |
P16383
|
FINISHED |
| Object | Dizi |
E386665
|
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: Dizi | Statement: [Kafa, hasNeighboringLanguage, Dizi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dizi Context triple: [Kafa, hasNeighboringLanguage, Dizi]
-
A.
Dizi
chosen
Dizi is an Omotic language spoken primarily by the Dizi people in southwestern Ethiopia.
-
B.
Seri
The Seri are an Indigenous people of northwestern Mexico, traditionally living along the Gulf of California coast and known for their rich maritime culture, distinctive language, and artisanal crafts.
-
C.
Dassu
Dassu is a town in Pakistan’s Khyber Pakhtunkhwa province that serves as the administrative center of Upper Kohistan District in the mountainous Kohistan region.
-
D.
Milliyet
Milliyet is a major Turkish daily newspaper known for its national coverage and influential role in Turkey’s media landscape.
-
E.
Kohan
Kohan is a surname most prominently associated with American television writer and producer Jenji Kohan, known for creating the series "Weeds" and "Orange Is the New Black."
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156d0d55c8190af59ff169e8add78 |
completed | April 16, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe76df6481909f8246099faa377a |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:53 a.m.