Triple
T19544154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toshirō Mifune |
E488993
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | High and Low |
—
|
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: High and Low | Statement: [Toshirō Mifune, notableWork, High and Low]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: High and Low Context triple: [Toshirō Mifune, notableWork, High and Low]
-
A.
High and Low
chosen
High and Low is a 1963 Japanese crime thriller film by Akira Kurosawa that explores class disparity and moral conflict through a tense kidnapping drama.
-
B.
High and Low
High and Low is a notable musical composition by American songwriter and composer Arthur Schwartz.
-
C.
High Low and In Between
High Low and In Between is a country song recorded by American singer Mark Wills, known for its emotional storytelling and traditional country sound.
-
D.
How Low
"How Low" is a popular hip-hop single by American rapper Ludacris, known for its catchy hook and heavy club-oriented production.
-
E.
Hi Lo
"Hi Lo" is a segment from the British rockumentary film and television series "The Kids Are Alright," which chronicles the history and performances of the Who.
- 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63875cf40819088db7c7969be1e3d |
completed | April 20, 2026, 2:30 p.m. |
Created at: April 10, 2026, 1:41 p.m.