Triple
T6864582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ezaki Glico Co., Ltd. |
E158365
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object |
Bisco
Bisco is a popular Japanese biscuit snack brand known for its cream-filled sandwich cookies marketed as a nutritious treat for children.
|
E623308
|
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: Bisco | Statement: [Ezaki Glico Co., Ltd., hasBrand, Bisco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bisco Context triple: [Ezaki Glico Co., Ltd., hasBrand, Bisco]
-
A.
Bitchois
Bitchois is the French demonym for inhabitants of the town of Bitche in northeastern France.
-
B.
Chicolini
Chicolini is a bumbling, fast-talking character played by Chico Marx in the Marx Brothers film "Duck Soup," serving as a comic foil in the political satire.
-
C.
Bibemi
Bibemi is a town located in the North Region of Cameroon, known as a local administrative and trading center in the area.
-
D.
Kachidoki
Kachidoki is a waterfront district in Tokyo’s Chūō ward known for its high-rise residential towers, proximity to the Sumida River, and convenient access to central Tokyo.
-
E.
Rolo
Rolo is a diminutive form of the given name Roland, often used as a familiar or affectionate nickname.
- 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: Bisco Triple: [Ezaki Glico Co., Ltd., hasBrand, Bisco]
Generated description
Bisco is a popular Japanese biscuit snack brand known for its cream-filled sandwich cookies marketed as a nutritious treat for children.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bisco Target entity description: Bisco is a popular Japanese biscuit snack brand known for its cream-filled sandwich cookies marketed as a nutritious treat for children.
-
A.
Bitchois
Bitchois is the French demonym for inhabitants of the town of Bitche in northeastern France.
-
B.
Chicolini
Chicolini is a bumbling, fast-talking character played by Chico Marx in the Marx Brothers film "Duck Soup," serving as a comic foil in the political satire.
-
C.
Bibemi
Bibemi is a town located in the North Region of Cameroon, known as a local administrative and trading center in the area.
-
D.
Kachidoki
Kachidoki is a waterfront district in Tokyo’s Chūō ward known for its high-rise residential towers, proximity to the Sumida River, and convenient access to central Tokyo.
-
E.
Rolo
Rolo is a diminutive form of the given name Roland, often used as a familiar or affectionate nickname.
- 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_69c68830cdbc8190a8301c7a9d9f651a |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d88af6d88190ac9faa32fa1bfa0e |
completed | March 27, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72ff153d48190a4b0d4e403457fe8 |
completed | March 28, 2026, 1:33 a.m. |
| NEDg | Description generation | batch_69c730e882308190a3fbc61245941338 |
completed | March 28, 2026, 1:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7316a9cc081908c088725cb7626e8 |
completed | March 28, 2026, 1:39 a.m. |
Created at: March 27, 2026, 2:21 p.m.