Triple
T4173568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jonah Bobo |
E86422
|
entity |
| Predicate | familyName |
P18
|
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: [Jonah Bobo, familyName, Bobo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bobo Context triple: [Jonah Bobo, familyName, Bobo]
-
A.
Bobo
chosen
Bobo is a major Mande language spoken primarily in parts of West Africa, notably in Burkina Faso and Mali.
-
B.
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."
-
C.
Teddy
Teddy is Mr. Bean’s beloved brown teddy bear, a silent yet expressive companion that often serves as his confidant and playmate in the comedy series.
-
D.
Buddy
Buddy is the young boy narrator and central figure in Truman Capote’s autobiographical short story “A Christmas Memory,” reflecting the author’s own childhood experiences.
-
E.
Buddy
Buddy is the cheerful, childlike main character of the Christmas comedy film "Elf," known for being a human raised at the North Pole by Santa’s elves.
- 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_69aed93de98c8190ad838ce507b77c8a |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af02e7d30081909774a3e2a2132d6a |
completed | March 9, 2026, 5:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b57f5428a0819082aaea8950a7c039 |
completed | March 14, 2026, 3:31 p.m. |
Created at: March 9, 2026, 3:45 p.m.