Triple
T9760104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maurice Chevalier |
E236646
|
entity |
| Predicate | notableSong |
P4
|
FINISHED |
| Object | Mimi |
E150188
|
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: Mimi | Statement: [Maurice Chevalier, notableSong, Mimi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mimi Context triple: [Maurice Chevalier, notableSong, Mimi]
-
A.
Mimi
chosen
Mimi is a common affectionate diminutive or nickname for the given name Marie.
-
B.
Misti
Misti is a prominent, snow-capped stratovolcano overlooking the city of Arequipa in southern Peru.
-
C.
Mimili
Mimili is a remote Aboriginal community in South Australia, home primarily to Pitjantjatjara people and known for its strong cultural traditions and art.
-
D.
Mija
Mija is the brave young girl protagonist of the film "Okja," who risks everything to rescue her genetically engineered super-pig friend from a powerful corporation.
-
E.
Mimi Hii
Mimi Hii is a prominent chemist known for her research in catalysis and sustainable chemistry, holding a prestigious professorship at Imperial College London.
- 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_69ca84d64f6c8190a4ed4e9f5936eda5 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda049995c81908569ec61805642b2 |
completed | April 1, 2026, 10:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bce1c5908190a89815fa2b7d90c8 |
completed | April 5, 2026, 1:37 a.m. |
Created at: March 30, 2026, 8:25 p.m.