Triple
T23136701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomie |
E577341
|
entity |
| Predicate | franchise |
P1500
|
FINISHED |
| Object | Tomie franchise |
—
|
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: Tomie franchise | Statement: [Tomie, franchise, Tomie franchise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tomie franchise Context triple: [Tomie, franchise, Tomie franchise]
-
A.
Tomie
chosen
Tomie is a Japanese horror manga character created by Junji Ito, known as a beautiful, immortal girl who drives people to madness and murder through her supernatural influence.
-
B.
Tomi
Tomi is a city in Nagano Prefecture, Japan, known for its scenic rural landscapes and proximity to the active volcano Mount Asama.
-
C.
T.O.M.
T.O.M. is the robotic host and mascot of Cartoon Network’s Toonami programming block, known for introducing shows and guiding viewers through the lineup in a futuristic spaceship setting.
-
D.
Wilma Tenderfoot series
The Wilma Tenderfoot series is a humorous children's mystery book series following an orphaned girl who dreams of becoming a detective, written by British author Emma Kennedy.
-
E.
Tomomi
Tomomi is a Japanese given name that can be used for people of any gender.
- 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_69e245f8e6248190ba3d58e068b4dccb |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e8c33308190a44f98a7aab3b670 |
completed | April 29, 2026, 4:52 a.m. |
Created at: April 17, 2026, 4 p.m.