Triple
T19218585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Beatles LOVE |
E480548
|
entity |
| Predicate | authorizedBy |
P29
|
FINISHED |
| Object | Yoko Ono Lennon |
—
|
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: Yoko Ono Lennon | Statement: [The Beatles LOVE, authorizedBy, Yoko Ono Lennon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yoko Ono Lennon Context triple: [The Beatles LOVE, authorizedBy, Yoko Ono Lennon]
-
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.
Naoko Ono
Naoko Ono is a Japanese individual notable enough to be recognized as a prominent bearer of the surname Ono.
-
C.
Yoko Tsukasa
Yoko Tsukasa is a Japanese actress best known internationally for her role in Akira Kurosawa’s samurai film "Yojimbo."
-
D.
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.
-
E.
Yoko
Yoko is a Japanese given name commonly used for women and borne by various notable figures in arts, literature, and entertainment.
- 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_69d8e8cb8c348190b52075823911c869 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fa3c552c8190b22844e5b7adfc50 |
completed | April 20, 2026, 10:04 a.m. |
Created at: April 10, 2026, 1:23 p.m.