Triple
T15974602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bonnie |
E387411
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object |
Bon-Bon
Bon-Bon is a common affectionate nickname, often used for people named Bonnie or for characters in popular media.
|
E1185637
|
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: Bon-Bon | Statement: [Bonnie, hasNickname, Bon-Bon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bon-Bon Context triple: [Bonnie, hasNickname, Bon-Bon]
-
A.
Bombon
Bombon is a municipality, likely in the Philippines, known as the namesake and administrative area associated with the Bombon dialect.
-
B.
Petit Bé fort
Petit Bé fort is a tidal island fortress off Saint-Malo, France, built in the late 17th century as part of the town’s coastal defenses.
-
C.
Chouchou
Chouchou was the affectionate nickname of Claude Debussy’s young daughter, to whom he dedicated his piano suite "Children’s Corner."
-
D.
Bisco
Bisco is a popular Japanese biscuit snack brand known for its cream-filled sandwich cookies marketed as a nutritious treat for children.
-
E.
Rosaroll
Rosaroll was an Italian Philhellene and military figure known for supporting the Greek War of Independence in the early 19th century.
- 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: Bon-Bon Triple: [Bonnie, hasNickname, Bon-Bon]
Generated description
Bon-Bon is a common affectionate nickname, often used for people named Bonnie or for characters in popular media.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bon-Bon Target entity description: Bon-Bon is a common affectionate nickname, often used for people named Bonnie or for characters in popular media.
-
A.
Bombon
Bombon is a municipality, likely in the Philippines, known as the namesake and administrative area associated with the Bombon dialect.
-
B.
Petit Bé fort
Petit Bé fort is a tidal island fortress off Saint-Malo, France, built in the late 17th century as part of the town’s coastal defenses.
-
C.
Chouchou
Chouchou was the affectionate nickname of Claude Debussy’s young daughter, to whom he dedicated his piano suite "Children’s Corner."
-
D.
Bisco
Bisco is a popular Japanese biscuit snack brand known for its cream-filled sandwich cookies marketed as a nutritious treat for children.
-
E.
Rosaroll
Rosaroll was an Italian Philhellene and military figure known for supporting the Greek War of Independence in the early 19th century.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1572b667c8190b28d0556e45422bb |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe8ce7788190a3e0aefc9a29d58a |
completed | May 9, 2026, 11:09 p.m. |
| NEDg | Description generation | batch_69ffbf50d5fc8190a045846f046e04cf |
completed | May 9, 2026, 11:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffbfb1ed7c81908771dedce172707a |
completed | May 9, 2026, 11:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.