Triple
T6690679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zabana |
E152617
|
entity |
| Predicate | hasNeighboringLanguage |
P16383
|
FINISHED |
| Object | Gao |
E529790
|
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: Gao | Statement: [Zabana, hasNeighboringLanguage, Gao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gao Context triple: [Zabana, hasNeighboringLanguage, Gao]
-
A.
Gao
Gao is a Chinese surname historically associated with the Jewish community of Kaifeng, one of the oldest Jewish diasporas in China.
-
B.
Gao
Gao is a historic city in eastern Mali that served as a major trading center and former capital of the Songhai Empire along the Niger River.
-
C.
Gao
chosen
Gao is an Oceanic language of the Southeast Solomonic group spoken in the Solomon Islands.
-
D.
Gao Yin
Gao Yin was an emperor of the Northern Qi dynasty in ancient China, known for his brief and turbulent reign amid intense court intrigue and power struggles.
-
E.
Guangyi
Guangyi was a warship that served in China's late 19th-century Beiyang Fleet, one of the Qing dynasty's principal modern naval forces.
- 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_69c6880687b08190805278b504d1c92c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6b15276208190b4c0e90ca337d2b4 |
completed | March 27, 2026, 4:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6f7b519148190a810d0eee7cda734 |
completed | March 27, 2026, 9:33 p.m. |
Created at: March 27, 2026, 2:04 p.m.