Triple
T6528145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guineans |
E151357
|
entity |
| Predicate | traditionalInstrument |
P8530
|
FINISHED |
| Object | kora |
E201168
|
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: kora | Statement: [Guineans, traditionalInstrument, kora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: kora Context triple: [Guineans, traditionalInstrument, kora]
-
A.
kora
chosen
The kora is a West African 21-string harp-lute traditionally played by griots and widely associated with the musical heritage of Mali.
-
B.
KORL
KORL is the ICAO airport code for Orlando Executive Airport, a public airport serving the Orlando, Florida area.
-
C.
Korekore
Korekore is a major dialect of the Shona language spoken primarily in northern Zimbabwe.
-
D.
Kors
Kors is the surname of American fashion designer Michael Kors, known for his eponymous luxury brand.
-
E.
kira
Kira is the traditional ankle-length dress worn by Ngalop women in Bhutan, typically wrapped and fastened with a belt and paired with a short jacket.
- 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_69c687f522748190b3058405553cdabd |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ada9c8408190b1bc327985366be9 |
completed | March 27, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d52ca9988190addfdae6d7b53a6e |
completed | March 27, 2026, 7:06 p.m. |
Created at: March 27, 2026, 1:46 p.m.