Triple
T4027293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enemies, A Love Story |
E83621
|
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: [Enemies, A Love Story, mainCharacter, Masha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Masha Context triple: [Enemies, A Love Story, mainCharacter, Masha]
-
A.
Masha
chosen
Masha is a diminutive and affectionate Russian form of the given name Mary (Maria).
-
B.
Misha
Misha is the bear mascot of the 1980 Moscow Summer Olympics, widely remembered for its iconic, sentimental farewell during the closing ceremony.
-
C.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
-
D.
Aloysya
Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
-
E.
Mishenka
Mishenka is a Russian affectionate diminutive form of the male given name Mikhail.
- 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_69aed92e29ac819080f7a98b594fec05 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaeec44881909a6c008eeae204df |
completed | March 9, 2026, 4:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b556325c48819099d4cb5c2049d7e7 |
completed | March 14, 2026, 12:36 p.m. |
Created at: March 9, 2026, 3:36 p.m.