Triple
T10933967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Askunu |
E258279
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Ashkuni |
E228158
|
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: Ashkuni | Statement: [Askunu, hasAlternativeName, Ashkuni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ashkuni Context triple: [Askunu, hasAlternativeName, Ashkuni]
-
A.
Ashkun
chosen
Ashkun is a Nuristani language spoken by the Ashkun people in parts of eastern Afghanistan.
-
B.
Samalkha
Samalkha is a town in the northern Indian state of Haryana, known for its industrial activity and location along major transport routes.
-
C.
Abaknon
Abaknon is an Austronesian language spoken by the Abaknon people, primarily on Capul Island in Northern Samar, Philippines.
-
D.
Sharazan
"Sharazan" is a popular Italian pop song performed by the duo Al Bano and Romina Power, known for its melodic style and romantic themes.
-
E.
Akhsar
Akhsar is a heroic figure from the Nart sagas, the traditional epic folklore of the peoples of the Caucasus.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770ae073881909720febe9f5f296a |
completed | April 9, 2026, 9:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e23bee9b208190aee8f938dff3f234 |
completed | April 17, 2026, 1:55 p.m. |
Created at: April 8, 2026, 9:23 p.m.