Triple
T3070272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cup Noodles Museum |
E64002
|
entity |
| Predicate | dedicatedTo |
P500
|
FINISHED |
| Object | Cup Noodles |
E147267
|
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: Cup Noodles | Statement: [Cup Noodles Museum, dedicatedTo, Cup Noodles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cup Noodles Context triple: [Cup Noodles Museum, dedicatedTo, Cup Noodles]
-
A.
Ramenki
Ramenki is a Moscow Metro station serving the Kalininsko–Solntsevskaya Line in the Ramenki District of western Moscow, Russia.
-
B.
Maggi
chosen
Maggi is a popular global food brand best known for its instant noodles, seasonings, and convenience products.
-
C.
Prego
Prego is a popular American brand of pasta sauces known for its thick, tomato-based varieties and wide range of flavors.
-
D.
kishimen noodles
Kishimen noodles are a type of flat, broad udon noodle from Japan, especially associated with Nagoya cuisine.
-
E.
dan zai noodles
Dan zai noodles are a traditional Taiwanese noodle dish, especially associated with Tainan, featuring thin noodles in a savory broth typically topped with minced pork and shrimp.
- 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_69ad857a8aec8190bfdfd9c14554ac5a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada100f0b8819095da366fdc6803a8 |
completed | March 8, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f87f6a3881908ae313f62ff13159 |
completed | March 11, 2026, 11:19 p.m. |
Created at: March 8, 2026, 3:02 p.m.