Triple
T13974607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sayawa |
E336152
|
entity |
| Predicate | hasEthnonymVariant |
P57538
|
FINISHED |
| Object | Zaar |
E788777
|
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: Zaar | Statement: [Sayawa, hasEthnonymVariant, Zaar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zaar Context triple: [Sayawa, hasEthnonymVariant, Zaar]
-
A.
Zaar
chosen
Zaar is a West Chadic language spoken primarily in Bauchi State, Nigeria.
-
B.
Zaat
Zaat is a satirical Egyptian novel by Sonallah Ibrahim that critiques modern Egyptian society through the life of a middle-class woman navigating political and social upheavals.
-
C.
Zardoz
Zardoz is a 1974 science fiction film directed by John Boorman, known for its surreal, dystopian vision and starring Sean Connery in one of his most unconventional roles.
-
D.
Zonaras
Zonaras was a 12th-century Byzantine chronicler and former imperial official best known for his Epitome of Histories, a major source for earlier lost works on Roman and Byzantine history.
-
E.
Zezuru
Zezuru is a major dialect of the Shona language spoken primarily in central and northern Zimbabwe.
- 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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8fd6d48190a157eae8df3a2f3a |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1e1eb108190b3c0739b94556172 |
completed | May 6, 2026, 8:17 p.m. |
Created at: April 9, 2026, 10:18 p.m.