Triple
T8458929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gabriel Mann |
E199990
|
entity |
| Predicate | appearedIn |
P795
|
FINISHED |
| Object | The Ramen Girl |
E732796
|
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: The Ramen Girl | Statement: [Gabriel Mann, appearedIn, The Ramen Girl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Ramen Girl Context triple: [Gabriel Mann, appearedIn, The Ramen Girl]
-
A.
The Ramen Girl
chosen
The Ramen Girl is a 2008 romantic comedy-drama film about an American woman in Tokyo who finds purpose and connection by training under a stern ramen chef.
-
B.
Sakumono
Sakumono is a coastal suburban community in the Greater Accra Region of Ghana, known for its residential estates and proximity to the Sakumono Lagoon and beach.
-
C.
Ramenki
Ramenki is a Moscow Metro station serving the Kalininsko–Solntsevskaya Line in the Ramenki District of western Moscow, Russia.
-
D.
Yo! Sushi
Yo! Sushi is a UK-based restaurant chain known for its conveyor-belt served Japanese-inspired dishes, particularly sushi.
-
E.
The Noodle Maker
The Noodle Maker is a satirical novel by Chinese writer Ma Jian that critiques contemporary Chinese society through darkly comic, interwoven stories.
- 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_69ca83198c4c8190a337bf717d1813f5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe49e036c8190abb50ca272266ca6 |
completed | March 31, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1dea01c481909496ebfca4e9916e |
completed | April 2, 2026, 7:42 a.m. |
Created at: March 30, 2026, 6:10 p.m.