Triple
T6771782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Direction Nationale des Langues Nationales du Mali |
E155060
|
entity |
| Predicate | worksOnLanguage |
P18404
|
FINISHED |
| Object | Bobo |
E302231
|
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: Bobo | Statement: [Direction Nationale des Langues Nationales du Mali, worksOnLanguage, Bobo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bobo Context triple: [Direction Nationale des Langues Nationales du Mali, worksOnLanguage, Bobo]
-
A.
Bobo
chosen
Bobo is a major Mande language spoken primarily in parts of West Africa, notably in Burkina Faso and Mali.
-
B.
Babo
Babo is a central character in Herman Melville’s novella "Benito Cereno," known as the cunning leader of a slave revolt who manipulates appearances aboard a Spanish slave ship.
-
C.
Teddy
Teddy is a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
-
D.
Teddy
Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
-
E.
Teddy
Teddy is a recurring character on the animated TV show "Bob's Burgers," known as the Belcher family's loyal but somewhat bumbling handyman and regular customer.
- 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_69c68812ef7c819099369f51febb725c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d7c94bac8190ae4b236d1b04bec9 |
completed | March 27, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c712c75b9c819099b0be616925a0b9 |
completed | March 27, 2026, 11:29 p.m. |
Created at: March 27, 2026, 2:13 p.m.