Triple
T5705968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Am Legend (film) |
E125784
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Mark Protosevich |
E197176
|
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: Mark Protosevich | Statement: [I Am Legend (film), screenwriter, Mark Protosevich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Protosevich Context triple: [I Am Legend (film), screenwriter, Mark Protosevich]
-
A.
Mark Protosevich
chosen
Mark Protosevich is an American screenwriter known for his work on major genre films such as "Thor" and "I Am Legend."
-
B.
Kirill Shubsky
Kirill Shubsky is a Russian businessman known primarily as the husband of actress and model Anastasia Shubskaya.
-
C.
Nikita Anisimov
Nikita Anisimov is a Russian academic and university administrator who serves as the rector of the National Research University Higher School of Economics (HSE) in Moscow.
-
D.
Adam Bielecki
Adam Bielecki is a Polish high-altitude mountaineer renowned for pioneering bold winter ascents in the Himalayas and Karakoram.
-
E.
Tyler Matakevich
Tyler Matakevich is an American football linebacker and special teams standout in the NFL, known for his prolific college career at Temple University and his later role with teams such as the Buffalo Bills.
- 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_69c0082d6fe48190b777fb383769e5c8 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02459cd18819080fda0b481d11f08 |
completed | March 22, 2026, 5:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a666d788190a0f786d12391a44b |
completed | March 22, 2026, 9:08 p.m. |
Created at: March 22, 2026, 3:45 p.m.