Triple
T22241085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rushani |
E549723
|
entity |
| Predicate | closelyRelatedTo |
P37
|
FINISHED |
| Object | Oroshori |
—
|
NE NERFINISHED |
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: Oroshori | Statement: [Rushani, closelyRelatedTo, Oroshori]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oroshori Context triple: [Rushani, closelyRelatedTo, Oroshori]
-
A.
Oroshori
chosen
Oroshori is an Eastern Iranian Pamiri language spoken in parts of Tajikistan and Afghanistan, closely related to Shughni and sharing many linguistic features with it.
-
B.
Oreshura
Oreshura is a Japanese romantic comedy light novel and anime series that follows a high school boy roped into a fake relationship with a popular girl to fend off unwanted romantic attention.
-
C.
Nakoruru
Nakoruru is a popular Samurai Shodown character known as a nature-loving Ainu shrine maiden who fights alongside her hawk and wolf companions.
-
D.
Aishō
Aishō is a town in Shiga Prefecture, Japan, known for its rural character and historical sites.
-
E.
Kudanshita
Kudanshita is a district and major subway station area in central Tokyo known for its proximity to the Imperial Palace, Yasukuni Shrine, and several universities and office buildings.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e41d9408190bd770cf282e22753 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f132140ed481909ab0d4022756a4ba |
completed | April 28, 2026, 10:17 p.m. |
Created at: April 16, 2026, 8:38 p.m.