Triple
T10210615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mari Yoriko Sabusawa |
E242316
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mari |
E458791
|
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: Mari | Statement: [Mari Yoriko Sabusawa, givenName, Mari]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mari Context triple: [Mari Yoriko Sabusawa, givenName, Mari]
-
A.
Mari
Mari is a Uralic language spoken by the Mari people, primarily in the Mari El Republic of Russia.
-
B.
Mari
Mari is an ancient Mesopotamian city-state on the Euphrates River, renowned for its well-preserved palace complex and thousands of cuneiform tablets that illuminate early Syrian and Mesopotamian history.
-
C.
Mari
chosen
Mari is a feminine given name, often used as a short form of names like Marigold, Mary, or Maria in various cultures.
-
D.
Mari
Mari is a character in Paulo Coelho's novel "Veronika Decides to Die," portrayed as a fellow patient in the mental institution who struggles with anxiety and societal expectations.
-
E.
Marla
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d395fbed008190b66996f5bb397853 |
completed | April 6, 2026, 11:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d652d8088c819084040883f2ab9dc6 |
completed | April 8, 2026, 1:06 p.m. |
Created at: April 6, 2026, 11:01 a.m.