Triple
T2317781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Owen |
E51105
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Owens |
E51105
|
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: Owens | Statement: [Owen, hasVariant, Owens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Owens Context triple: [Owen, hasVariant, Owens]
-
A.
Owen
chosen
Owen is a common Welsh-origin surname borne by many people, including the renowned World War I poet Wilfred Owen.
-
B.
Ochs
Ochs is a surname most prominently associated with the Ochs-Sulzberger family, the longtime publishers and owners of The New York Times.
-
C.
Hayes
Hayes is a suburban district in southeast London, England, known for its residential character and green spaces within the London Borough of Bromley.
-
D.
McCauley
McCauley is the maiden surname of Rosa Parks, the prominent American civil rights activist known for her pivotal role in the Montgomery bus boycott.
-
E.
Tucker
Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc62fa60c8190b4859ce296ea4177 |
completed | March 7, 2026, 6:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae8964902081909070dd03ccb7cf1f |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:49 p.m.