Triple
T7006146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neo-Aramaic languages |
E162460
|
entity |
| Predicate | includes |
P1393
|
FINISHED |
| Object | Lishan Didan |
E603330
|
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: Lishan Didan | Statement: [Neo-Aramaic languages, includes, Lishan Didan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lishan Didan Context triple: [Neo-Aramaic languages, includes, Lishan Didan]
-
A.
Lishan Didan
chosen
Lishan Didan is a Jewish Neo-Aramaic language traditionally spoken by Kurdish and Azerbaijani Jews from the regions of northwestern Iran and eastern Turkey.
-
B.
Yishan
Yishan was a Qing dynasty military commander and noble who led Chinese forces during the First Opium War against Britain.
-
C.
Tudigong
Tudigong is a widely venerated Chinese earth god and local tutelary deity associated with protecting land, villages, and community welfare.
-
D.
Mount Wangwu
Mount Wangwu is a renowned scenic mountain area in China, celebrated for its dramatic landscapes, cultural legends, and historical significance within the Taihang mountain range.
-
E.
Chinese Tower
The Chinese Tower is a famous multi-story wooden pagoda-style structure and beer garden located within Munich’s Englischer Garten park.
- 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_69c6885928148190ae31909fbb5e9849 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc34b5a88190a793e07dd4d0018b |
completed | March 27, 2026, 7:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a3f5a088190bd0fa2080a8fa648 |
completed | March 28, 2026, 5:42 a.m. |
Created at: March 27, 2026, 2:33 p.m.