Triple
T2607751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tainan |
E58700
|
entity |
| Predicate | cuisineSpecialty |
P1016
|
FINISHED |
| Object |
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.
|
E282409
|
NE FINISHED |
How this triple was built (4 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: dan zai noodles | Statement: [Tainan, cuisineSpecialty, dan zai noodles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: dan zai noodles Context triple: [Tainan, cuisineSpecialty, dan zai noodles]
-
A.
Ramenki
Ramenki is a Moscow Metro station serving the Kalininsko–Solntsevskaya Line in the Ramenki District of western Moscow, Russia.
-
B.
kishimen noodles
Kishimen noodles are a type of flat, broad udon noodle from Japan, especially associated with Nagoya cuisine.
-
C.
Mandu
Mandu is a historic fortified city in central India renowned for its Afghan-era architecture, romantic legends, and scenic hilltop setting.
-
D.
Rozogi
Rozogi is a village in northern Poland that serves as a local administrative and service center within the Warmian-Masurian Voivodeship.
-
E.
Prego
Prego is a popular American brand of pasta sauces known for its thick, tomato-based varieties and wide range of flavors.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: dan zai noodles Triple: [Tainan, cuisineSpecialty, dan zai noodles]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: dan zai noodles Target entity description: 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.
-
A.
Ramenki
Ramenki is a Moscow Metro station serving the Kalininsko–Solntsevskaya Line in the Ramenki District of western Moscow, Russia.
-
B.
kishimen noodles
Kishimen noodles are a type of flat, broad udon noodle from Japan, especially associated with Nagoya cuisine.
-
C.
Mandu
Mandu is a historic fortified city in central India renowned for its Afghan-era architecture, romantic legends, and scenic hilltop setting.
-
D.
Rozogi
Rozogi is a village in northern Poland that serves as a local administrative and service center within the Warmian-Masurian Voivodeship.
-
E.
Prego
Prego is a popular American brand of pasta sauces known for its thick, tomato-based varieties and wide range of flavors.
- F. None of above. chosen
Provenance (5 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_69ab4ac3523881909679750c9f8c2dec |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd86769b88190935151a01c1ac855 |
completed | March 7, 2026, 7:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af83de926c81909e45160f9fc78799 |
completed | March 10, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_69af8501adc4819092035d7e55524fc8 |
completed | March 10, 2026, 2:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69af85aa5f548190a4ce1a4ff38ca5e3 |
completed | March 10, 2026, 2:44 a.m. |
Created at: March 6, 2026, 9:49 p.m.