Triple
T5881524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Seagull |
E130758
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Masha |
E370387
|
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: Masha | Statement: [The Seagull, mainCharacter, Masha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Masha Context triple: [The Seagull, mainCharacter, Masha]
-
A.
Masha
chosen
Masha is a diminutive and affectionate Russian form of the given name Mary (Maria).
-
B.
Malvina
Malvina is a feminine given name of Scottish origin, often associated with literary and romantic traditions.
-
C.
Misha
Misha is the bear mascot of the 1980 Moscow Summer Olympics, widely remembered for its iconic, sentimental farewell during the closing ceremony.
-
D.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
-
E.
Grushenka
Grushenka is a central female character in Fyodor Dostoevsky's novel "The Brothers Karamazov," known for her complex mix of sensuality, capriciousness, and capacity for moral and spiritual transformation.
- 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_69c0085523688190bfd487479ce819e6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03635a41c819086f9a5df242777f2 |
completed | March 22, 2026, 6:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b13330c88190b10f33843f10f51f |
completed | March 23, 2026, 3:19 a.m. |
Created at: March 22, 2026, 3:57 p.m.