Triple
T11499691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zande language |
E272630
|
entity |
| Predicate | neighboringLanguages |
P16383
|
FINISHED |
| Object | Moru |
E905401
|
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: Moru | Statement: [Zande language, neighboringLanguages, Moru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moru Context triple: [Zande language, neighboringLanguages, Moru]
-
A.
Moru
chosen
Moru is a Central Sudanic language spoken primarily by the Moru people in South Sudan.
-
B.
Nambui
Nambui was a Mongol empress consort of the Yuan dynasty and a prominent wife of Kublai Khan, influential in the imperial court after the death of his first empress.
-
C.
Miyazya
Miyazya is one of the spring months in the Ethiopian calendar, roughly corresponding to April in the Gregorian calendar.
-
D.
Omura
Omura is a coastal city in western Japan known for its proximity to Nagasaki, Omura Bay, and its regional industrial and transportation hubs.
-
E.
Maku
Maku is a city in northwestern Iran known for its mountainous landscape and proximity to the Turkish border.
- 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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85de3e9c881909d6c55334f7a832d |
completed | April 10, 2026, 2:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e71362e59481909675a1a784dcf7fd |
completed | April 21, 2026, 6:04 a.m. |
Created at: April 8, 2026, 9:36 p.m.