Triple
T13631107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Egun |
E325718
|
entity |
| Predicate | hasExonym |
P4705
|
FINISHED |
| Object | Gungbe |
E916983
|
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: Gungbe | Statement: [Egun, hasExonym, Gungbe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gungbe Context triple: [Egun, hasExonym, Gungbe]
-
A.
Gengbe
chosen
Gengbe is a dialect of the Gbe language cluster spoken primarily in parts of West Africa, closely related to Ewe and other Gbe varieties.
-
B.
Gedo
Gedo is a region in southwestern Somalia known for its strategic location bordering Kenya and Ethiopia and its role within the federal state of Jubaland.
-
C.
Gongnie
Gongnie was the personal name of King You of Zhou, the last king of the Western Zhou dynasty in ancient China.
-
D.
Morungaba
Morungaba is a small municipality in the state of São Paulo, Brazil, known for its rural landscapes and integration into the economically significant Campinas metropolitan area.
-
E.
Gugino
Gugino is the surname of American actress Carla Gugino, known for her versatile roles in film and television.
- 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_69d8076beddc8190a53156f5bea77f5e |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbe9ea9088190a17270dec82bbcaa |
completed | April 12, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77fab07648190b3b3362a8ffa8961 |
completed | May 3, 2026, 5:02 p.m. |
Created at: April 9, 2026, 9:51 p.m.