Triple
T5705973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Am Legend (film) |
E125784
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Dash Mihok |
E286393
|
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: Dash Mihok | Statement: [I Am Legend (film), starring, Dash Mihok]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dash Mihok Context triple: [I Am Legend (film), starring, Dash Mihok]
-
A.
Dash Mihok
chosen
Dash Mihok is an American actor best known for his roles in films like "The Thin Red Line" and the TV series "Ray Donovan."
-
B.
Komae
Komae is a small residential city in Tokyo Metropolis, Japan, known for its suburban character and proximity to central Tokyo.
-
C.
Mamoru
Mamoru is a Japanese masculine given name commonly borne by notable figures in politics, arts, and entertainment.
-
D.
Hayato
Hayato is a masculine Japanese given name commonly used for boys and borne by various notable figures in politics, sports, and entertainment.
-
E.
Katsuya
Katsuya is a Japanese given name commonly used for males.
- 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.