Triple
T16882580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takeover |
E421456
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Yoko Ono |
E93289
|
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: Yoko Ono | Statement: [Takeover, writer, Yoko Ono]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yoko Ono Context triple: [Takeover, writer, Yoko Ono]
-
A.
Yoko Ono
chosen
Yoko Ono is a Japanese multimedia artist, musician, and peace activist known for her avant-garde work and her marriage and collaborations with John Lennon.
-
B.
Yoko Satō
Yoko Satō is a Japanese individual known for bearing the surname Satō, which is one of the most common family names in Japan.
-
C.
Yoko
Yoko is a Japanese given name commonly used for women and borne by various notable figures in arts, literature, and entertainment.
-
D.
Yoko Littner
Yoko Littner is a prominent, sharp-shooting heroine from the anime series "Tengen Toppa Gurren Lagann," known for her combat skills, distinctive appearance, and strong-willed personality.
-
E.
Joan Jonas
Joan Jonas is an influential American visual artist and pioneer of performance and video art whose experimental, multimedia works have significantly shaped contemporary art since the late 1960s.
- 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_69d889d470fc8190b4aec199636c0c56 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3bbbdffd8819093f1efd91f6d49dc |
completed | April 18, 2026, 5:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c2b684108190928afb0a2d1af038 |
completed | May 10, 2026, 5:39 p.m. |
Created at: April 10, 2026, 5:29 a.m.