Triple
T22370115
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Чайка |
E553016
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Masha |
—
|
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: Masha | Statement: [Чайка, character, Masha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Masha Context triple: [Чайка, character, Masha]
-
A.
Masha
Masha is a town in southwestern Ethiopia that serves as an administrative and commercial center in the Sheka Zone.
-
B.
Masha
chosen
Masha is a diminutive and affectionate Russian form of the given name Mary (Maria).
-
C.
Marichka
Marichka is a key supporting character in the dystopian film "Children of Men," known for helping protect the first pregnant woman in years.
-
D.
Mishka
Mishka is a reggae-influenced singer-songwriter known for his mellow, island-inspired sound and collaborations within the jam and surf music scenes.
-
E.
Mashenka
Mashenka is a Russian diminutive form of the female given name Maria, often used affectionately for girls and women.
- 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_69e11e4affcc8190ba7c27d29062558d |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f158032b748190ad36c7e3809304e9 |
completed | April 29, 2026, 12:59 a.m. |
Created at: April 16, 2026, 8:44 p.m.