Triple
T6995965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zezuru |
E162214
|
entity |
| Predicate | closelyRelatedTo |
P37
|
FINISHED |
| Object |
Korekore dialect
The Korekore dialect is a regional variety of the Shona language spoken primarily by the Korekore people in northern Zimbabwe.
|
E634464
|
NE FINISHED |
How this triple was built (4 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: Korekore dialect | Statement: [Zezuru, closelyRelatedTo, Korekore dialect]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Korekore dialect Context triple: [Zezuru, closelyRelatedTo, Korekore dialect]
-
A.
Kikai dialect
The Kikai dialect is a regional variety of the Amami language spoken on Kikai Island in Japan’s Ryukyu archipelago.
-
B.
Kuto-Kute dialect
The Kuto-Kute dialect is a regional variety of the Sasak language spoken on the island of Lombok in Indonesia.
-
C.
Kamia dialect
The Kamia dialect is a regional variety of the Ipai-Tipai language traditionally spoken by the Kamia (Kumeyaay) people of southern California and northern Baja California.
-
D.
Hkaku dialect
Hkaku dialect is a regional variety of the Jingpo language spoken by Jingpo communities in parts of Myanmar and neighboring areas.
-
E.
Gaika dialect
The Gaika dialect is a regional variety of the Xhosa language traditionally associated with the amaGqika subgroup in South Africa.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Korekore dialect Triple: [Zezuru, closelyRelatedTo, Korekore dialect]
Generated description
The Korekore dialect is a regional variety of the Shona language spoken primarily by the Korekore people in northern Zimbabwe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Korekore dialect Target entity description: The Korekore dialect is a regional variety of the Shona language spoken primarily by the Korekore people in northern Zimbabwe.
-
A.
Kikai dialect
The Kikai dialect is a regional variety of the Amami language spoken on Kikai Island in Japan’s Ryukyu archipelago.
-
B.
Kuto-Kute dialect
The Kuto-Kute dialect is a regional variety of the Sasak language spoken on the island of Lombok in Indonesia.
-
C.
Kamia dialect
The Kamia dialect is a regional variety of the Ipai-Tipai language traditionally spoken by the Kamia (Kumeyaay) people of southern California and northern Baja California.
-
D.
Hkaku dialect
Hkaku dialect is a regional variety of the Jingpo language spoken by Jingpo communities in parts of Myanmar and neighboring areas.
-
E.
Gaika dialect
The Gaika dialect is a regional variety of the Xhosa language traditionally associated with the amaGqika subgroup in South Africa.
- F. None of above. chosen
Provenance (5 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_69c68857ffc08190857dc62cd5253777 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbec259c8190bb4cfbc1ff6fc786 |
completed | March 27, 2026, 7:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a1fa11481908450978acc1e0913 |
completed | March 28, 2026, 5:41 a.m. |
| NEDg | Description generation | batch_69c76b84f5688190a0aef7cd8695c6b0 |
completed | March 28, 2026, 5:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c76be95ecc8190a57ff197f236d434 |
completed | March 28, 2026, 5:49 a.m. |
Created at: March 27, 2026, 2:32 p.m.