Triple
T7021260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oeagrus |
E162829
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Linus |
E9851
|
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: Linus | Statement: [Oeagrus, hasChild, Linus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Linus Context triple: [Oeagrus, hasChild, Linus]
-
A.
Linus
chosen
Linus is a given name most famously associated with Linus Pauling, the American chemist and two-time Nobel Prize laureate.
-
B.
Linus Caldwell
Linus Caldwell is a skilled but somewhat inexperienced and eager-to-prove-himself con artist and pickpocket who becomes a key member of Danny Ocean’s heist crew in the Ocean’s film series.
-
C.
Linus van Pelt
Linus van Pelt is a thoughtful, blanket-carrying child from the Peanuts comic strip, known for his philosophical insights and unwavering belief in the Great Pumpkin.
-
D.
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."
-
E.
Teddy
Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
- 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_69c6885b26248190a857541e3d10e299 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e1ec34a48190b64cafb94e2f8706 |
completed | March 27, 2026, 8 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7757686bc819089f6749e43bbb89d |
completed | March 28, 2026, 6:30 a.m. |
Created at: March 27, 2026, 2:35 p.m.