Triple
T3249047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fun and Fancy Free |
E68132
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Bongo
Bongo is an animated musical segment from Disney’s 1947 anthology film "Fun and Fancy Free," following the adventures of a circus bear who longs for freedom and love.
|
E340797
|
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 | Statement: [Fun and Fancy Free, hasPart, Bongo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bongo Context triple: [Fun and Fancy Free, hasPart, Bongo]
-
A.
Chaka
"Chaka" is the 1978 debut solo album by American singer Chaka Khan, showcasing her blend of funk, soul, and R&B.
-
B.
Binga
Binga is a town and district in northwestern Zimbabwe known for its location on the southern shores of Lake Kariba and its association with the Tonga people.
-
C.
Bangala
Bangala is a regional variety of the Bantu language Lingala, spoken primarily in parts of the Democratic Republic of the Congo and neighboring areas.
-
D.
Mvezo
Mvezo is a small rural village in South Africa’s Eastern Cape best known as the birthplace of Nelson Mandela.
-
E.
Kizombo
Kizombo is a regional dialect of the Kikongo language spoken by Bakongo communities in Central Africa.
- 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 Triple: [Fun and Fancy Free, hasPart, Bongo]
Generated description
Bongo is an animated musical segment from Disney’s 1947 anthology film "Fun and Fancy Free," following the adventures of a circus bear who longs for freedom and love.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bongo Target entity description: Bongo is an animated musical segment from Disney’s 1947 anthology film "Fun and Fancy Free," following the adventures of a circus bear who longs for freedom and love.
-
A.
Chaka
"Chaka" is the 1978 debut solo album by American singer Chaka Khan, showcasing her blend of funk, soul, and R&B.
-
B.
Binga
Binga is a town and district in northwestern Zimbabwe known for its location on the southern shores of Lake Kariba and its association with the Tonga people.
-
C.
Bangala
Bangala is a regional variety of the Bantu language Lingala, spoken primarily in parts of the Democratic Republic of the Congo and neighboring areas.
-
D.
Mvezo
Mvezo is a small rural village in South Africa’s Eastern Cape best known as the birthplace of Nelson Mandela.
-
E.
Kizombo
Kizombo is a regional dialect of the Kikongo language spoken by Bakongo communities in Central Africa.
- 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_69ad858e4c708190aa31d486cfee8a6a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaf3fc3c8819080ac95974581ca0e |
completed | March 8, 2026, 5:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2776934108190ac405ba5ebd47084 |
completed | March 12, 2026, 8:20 a.m. |
| NEDg | Description generation | batch_69b27c2c16188190af03674ead3944de |
completed | March 12, 2026, 8:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b27ca63b1c8190ac6f67aef6d2c7e1 |
completed | March 12, 2026, 8:43 a.m. |
Created at: March 8, 2026, 3:09 p.m.