Triple
T9996623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chief Bogo |
E197217
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Chief Bogo |
E197217
|
NE FINISHED |
How this triple was built (2 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: Chief Bogo | Statement: [Chief Bogo, fullName, Chief Bogo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chief Bogo Context triple: [Chief Bogo, fullName, Chief Bogo]
-
A.
Chief Bogo
chosen
Chief Bogo is a stern, no-nonsense Cape buffalo who serves as the chief of police in Disney's animated film "Zootopia."
-
B.
Chief Mahaska
Chief Mahaska was a 19th-century leader of the Iowa (Ioway) people known for his role in relations with U.S. authorities during the era of westward expansion.
-
C.
Baol
Baol was a precolonial Wolof kingdom in what is now Senegal, known for succeeding the Wolof Empire as a regional political and economic power.
-
D.
Chief Napi
Chief Napi is a character based on a figure from Blackfoot mythology, depicted as a wise and powerful Native leader in the 2017 film "Wonder Woman."
-
E.
Chief Yowlachie
Chief Yowlachie was a Native American actor and singer known for his roles in early 20th-century Hollywood films.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcb9af6a88190942cc4991bd373c1 |
completed | April 2, 2026, 1:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2583bd6948190b15a97187e81d958 |
completed | April 5, 2026, 12:40 p.m. |
Created at: March 30, 2026, 8:50 p.m.