Triple
T8450217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steven Soderbergh |
E199780
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Kimi |
E662973
|
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: Kimi | Statement: [Steven Soderbergh, notableWork, Kimi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kimi Context triple: [Steven Soderbergh, notableWork, Kimi]
-
A.
Kimi
chosen
Kimi is a 2022 techno-thriller film starring Zoë Kravitz as an agoraphobic tech worker who uncovers evidence of a violent crime through a virtual assistant’s audio data.
-
B.
Kima
Kima is a small settlement located on Car Nicobar Island in the Nicobar district of India’s Andaman and Nicobar Islands.
-
C.
Ukyo
Ukyo is a Japanese given name commonly used for males and borne by various real and fictional characters.
-
D.
Ōkimi
Ōkimi was the title used for the supreme ruler of early Japan’s Yamato state, a precursor to the later Japanese emperor.
-
E.
Kai
Kai is the fictional half-Japanese, half-English outcast and skilled warrior portrayed by Keanu Reeves in the fantasy samurai film "47 Ronin."
- 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_69ca8318231881908fd1bc1c4d45d286 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe44707b88190b3d8b30c45ef4496 |
completed | March 31, 2026, 3:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1dc85e48819083340d022d0dba9b |
completed | April 2, 2026, 7:42 a.m. |
Created at: March 30, 2026, 6:09 p.m.