Triple
T18584392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sgt. Frog |
E454199
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Tamama
Tamama is a hyperactive, childlike frog-like alien from the anime and manga series "Sgt. Frog," known for his split personality and obsessive devotion to Sergeant Keroro.
|
E1332353
|
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: Tamama | Statement: [Sgt. Frog, mainCharacter, Tamama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamama Context triple: [Sgt. Frog, mainCharacter, Tamama]
-
A.
Tama
Tama is a diminutive form of the given name Tamara, often used as a familiar or affectionate nickname.
-
B.
Tama
Tama is a region in western Tokyo, Japan, encompassing several suburban cities and towns that serve as residential and commercial areas for the greater Tokyo metropolis.
-
C.
Tama
Tama was a Japanese light cruiser of the Imperial Japanese Navy that served in World War II before being sunk during the Battle off Cape Engaño in 1944.
-
D.
Tama
Tama is a small city in central Iowa known for its proximity to the Meskwaki Settlement and its role as a local agricultural and community hub.
-
E.
Tohana
Tohana is a town and municipal council in the Fatehabad district of the Indian state of Haryana, known as an agricultural and trading center in the region.
- 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: Tamama Triple: [Sgt. Frog, mainCharacter, Tamama]
Generated description
Tamama is a hyperactive, childlike frog-like alien from the anime and manga series "Sgt. Frog," known for his split personality and obsessive devotion to Sergeant Keroro.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tamama Target entity description: Tamama is a hyperactive, childlike frog-like alien from the anime and manga series "Sgt. Frog," known for his split personality and obsessive devotion to Sergeant Keroro.
-
A.
Tama
Tama is a diminutive form of the given name Tamara, often used as a familiar or affectionate nickname.
-
B.
Tama
Tama is a region in western Tokyo, Japan, encompassing several suburban cities and towns that serve as residential and commercial areas for the greater Tokyo metropolis.
-
C.
Tama
Tama was a Japanese light cruiser of the Imperial Japanese Navy that served in World War II before being sunk during the Battle off Cape Engaño in 1944.
-
D.
Tama
Tama is a small city in central Iowa known for its proximity to the Meskwaki Settlement and its role as a local agricultural and community hub.
-
E.
Tohana
Tohana is a town and municipal council in the Fatehabad district of the Indian state of Haryana, known as an agricultural and trading center in the region.
- 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_69d8d38ae7e081908a98df1251842402 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e543d200dc8190b8797d731f4e4865 |
completed | April 19, 2026, 9:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a04f89193788190bd7334f80c23d0df |
completed | May 13, 2026, 10:17 p.m. |
| NEDg | Description generation | batch_6a04fe50f2c88190bba68d4c0025d0ef |
completed | May 13, 2026, 10:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a04ff3cef0481909eef191716f4c74d |
completed | May 13, 2026, 10:46 p.m. |
Created at: April 10, 2026, 11:44 a.m.