Triple
T12654078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Tano |
E302236
|
entity |
| Predicate | hasLanguage |
P15
|
FINISHED |
| Object | Anyi |
E617314
|
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: Anyi | Statement: [Central Tano, hasLanguage, Anyi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anyi Context triple: [Central Tano, hasLanguage, Anyi]
-
A.
Jinyu
Jinyu is a major variety of the Jin group of Chinese dialects spoken primarily in northern China, especially in Shanxi and surrounding regions.
-
B.
Anying
Anying is the given name of Mao Anying, the eldest son of Chinese leader Mao Zedong who was killed in action during the Korean War.
-
C.
Anyin Bia
chosen
Anyin Bia is a dialect of the Anyin language spoken by communities in parts of West Africa, particularly in Côte d'Ivoire and neighboring regions.
-
D.
Meilin
Meilin is the 13-year-old Chinese-Canadian protagonist of Pixar's animated film "Turning Red," who transforms into a giant red panda whenever her emotions run high.
-
E.
Yanyou
Yanyou was a reign era of the Yuan dynasty under Emperor Ayurbarwada Buyantu Khan in the early 14th century.
- 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_69d7bded71a88190bb76e2413af9ea66 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96160730c81909e1aa3efb51bf159 |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6719c18508190a9c28b2526e6b336 |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:18 p.m.