Triple
T14526748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bongo |
E340797
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object |
Bongo (bear)
Bongo (bear) is a Disney cartoon bear character best known from the 1947 animated featurette "Bongo," originally released as part of the anthology film "Fun and Fancy Free."
|
E1103859
|
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: Bongo (bear) | Statement: [Bongo, hasMainCharacter, Bongo (bear)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bongo (bear) Context triple: [Bongo, hasMainCharacter, Bongo (bear)]
-
A.
Kabuna
Kabuna is a small village located on the atoll of Tabiteuea in the island nation of Kiribati in the central Pacific Ocean.
-
B.
Bruiser the Bear
Bruiser the Bear is the costumed bear mascot who represents Baylor University’s athletic teams and school spirit.
-
C.
Komo
Komo is a town located in Hela Province in the Highlands region of Papua New Guinea.
-
D.
Komo
The Komo are an ethnic group indigenous to western Ethiopia, particularly associated with the Gambela Region, with their own distinct language and cultural traditions.
-
E.
Dongo
Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of World War II.
- 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: Bongo (bear) Triple: [Bongo, hasMainCharacter, Bongo (bear)]
Generated description
Bongo (bear) is a Disney cartoon bear character best known from the 1947 animated featurette "Bongo," originally released as part of the anthology film "Fun and Fancy Free."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bongo (bear) Target entity description: Bongo (bear) is a Disney cartoon bear character best known from the 1947 animated featurette "Bongo," originally released as part of the anthology film "Fun and Fancy Free."
-
A.
Kabuna
Kabuna is a small village located on the atoll of Tabiteuea in the island nation of Kiribati in the central Pacific Ocean.
-
B.
Bruiser the Bear
Bruiser the Bear is the costumed bear mascot who represents Baylor University’s athletic teams and school spirit.
-
C.
Komo
Komo is a town located in Hela Province in the Highlands region of Papua New Guinea.
-
D.
Komo
The Komo are an ethnic group indigenous to western Ethiopia, particularly associated with the Gambela Region, with their own distinct language and cultural traditions.
-
E.
Dongo
Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of World War II.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea050781881909ed685d94479bf99 |
completed | April 14, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a52260881909a0d85603666107d |
completed | May 8, 2026, 5:53 a.m. |
| NEDg | Description generation | batch_69fd7b58096881909d85a2b319acb595 |
completed | May 8, 2026, 5:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd7bd58a6881908479b7608b7f1f3a |
completed | May 8, 2026, 5:59 a.m. |
Created at: April 10, 2026, 1:22 a.m.