Triple
T11114832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Gumuz |
E262857
|
entity |
| Predicate | neighboringLanguages |
P16383
|
FINISHED |
| Object | Berta |
E255792
|
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: Berta | Statement: [Northern Gumuz, neighboringLanguages, Berta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berta Context triple: [Northern Gumuz, neighboringLanguages, Berta]
-
A.
Berta
Berta is a fictional character in Paulo Coelho’s novel "The Devil and Miss Prym," serving as one of the villagers whose life and choices reflect the book’s central moral and spiritual dilemmas.
-
B.
Berta
Berta is the sharp-tongued, no-nonsense housekeeper known for her sarcastic humor on the sitcom "Two and a Half Men."
-
C.
Berta
chosen
Berta is a Nilo-Saharan language spoken primarily in parts of Sudan and Ethiopia.
-
D.
Frieda
Frieda is a 1947 British drama film produced by Michael Balcon that explores post-World War II tensions and prejudice in England.
-
E.
Frieda
Frieda is a minor Peanuts character known for her naturally curly hair and prim personality, who appears alongside Charlie Brown and his friends.
- 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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79aa7254c8190abce35696ad2be03 |
completed | April 9, 2026, 12:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d7da99881908d38ea66c37dfb92 |
completed | April 19, 2026, 1:18 a.m. |
Created at: April 8, 2026, 9:27 p.m.