Triple
T8627449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuxi |
E204312
|
entity |
| Predicate | hasEthnicGroup |
P1898
|
FINISHED |
| Object | Hani |
E197135
|
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: Hani | Statement: [Yuxi, hasEthnicGroup, Hani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hani Context triple: [Yuxi, hasEthnicGroup, Hani]
-
A.
Hani
chosen
The Hani are an ethnic minority group in China, primarily known for their terraced rice farming, distinctive traditional dress, and rich folk culture in the mountainous regions of Yunnan.
-
B.
Hana
Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
-
C.
Hana
Hana is a common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
-
D.
Hana
Hana is a person known primarily as the romantic partner of Kip.
-
E.
Hana
Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
- 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_69ca834a4ea0819094970dceb9e389f3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc472ccc0c81908e0708c94a7cbe65 |
completed | March 31, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebbf918f08190a9b0469eae8e0e0d |
completed | April 2, 2026, 6:56 p.m. |
Created at: March 30, 2026, 6:27 p.m.