Triple
T13902296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Russell Tovey |
E334255
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Tovey |
E334255
|
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: Tovey | Statement: [Russell Tovey, familyName, Tovey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tovey Context triple: [Russell Tovey, familyName, Tovey]
-
A.
Tovey
chosen
Tovey is an English surname most notably associated with actor Russell Tovey, known for his work in British television, film, and theatre.
-
B.
Alsop
Alsop is an English-language surname borne by various notable individuals across fields such as music, journalism, and politics.
-
C.
Clementi
Clementi is a residential town and planning area in the western region of Singapore, known for its housing estates, schools, and transport links.
-
D.
Tippett
Tippett is a surname most notably associated with Phil Tippett, the acclaimed visual effects supervisor and stop-motion animator known for his work on films like Star Wars and Jurassic Park.
-
E.
Kenneth Woods
Kenneth Woods is a member of the Woods family, known primarily in relation to that family lineage.
- 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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de25d9c7a48190ad8fb0ca676f4f7b |
completed | April 14, 2026, 11:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c722e72081909090b2d64000ebd9 |
completed | May 3, 2026, 10:07 p.m. |
Created at: April 9, 2026, 10:15 p.m.