Triple
T17223943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gilak people |
E418060
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Galeshi (Deylami) |
E1159643
|
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: Galeshi (Deylami) | Statement: [Gilak people, hasDialect, Galeshi (Deylami)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Galeshi (Deylami) Context triple: [Gilak people, hasDialect, Galeshi (Deylami)]
-
A.
Galeshi
chosen
Galeshi is a local Iranian dialect spoken in parts of northern Iran, particularly in rural and mountainous areas of Mazandaran and Gilan provinces.
-
B.
Gereshk
Gereshk is a strategically important town in southern Afghanistan known for its location along the Helmand River and the main highway connecting Kandahar to Herat.
-
C.
Ghilji
Ghilji are one of the largest and historically influential Pashtun tribal confederations, prominent in the politics and power struggles of Afghanistan.
-
D.
Alghisi
Alghisi is an Italian surname associated with individuals such as Giuditta Alghisi.
-
E.
Gaziemir
Gaziemir is a district of İzmir, Turkey, known for its proximity to the city’s main international airport and its role as a growing residential and commercial hub.
- 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_69d886d779488190b131369541c04e7d |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42ddfe3bc8190b22cee4fc0590b74 |
completed | April 19, 2026, 1:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0167596ab481909df59ce68c7f640e |
completed | May 11, 2026, 5:21 a.m. |
Created at: April 10, 2026, 5:38 a.m.