Triple
T11605685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugh Griffith |
E275252
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Hugh Griffith |
E275252
|
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: Hugh Griffith | Statement: [Hugh Griffith, name, Hugh Griffith]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hugh Griffith Context triple: [Hugh Griffith, name, Hugh Griffith]
-
A.
Hugh Griffith
chosen
Hugh Griffith was a Welsh character actor best known for his exuberant, scene-stealing performances in mid-20th-century British and American films, including his Oscar-winning role in "Ben-Hur."
-
B.
Guy Moore
Guy Moore is a film editor known for his work on the James Bond movie "Tomorrow Never Dies."
-
C.
Joss Ackland
Joss Ackland is a distinguished English actor known for his extensive film, television, and stage career, often portraying authoritative or villainous characters.
-
D.
Roger Lloyd-Pack
Roger Lloyd-Pack was an English actor best known for his comedic roles in British television, particularly as Trigger in "Only Fools and Horses."
-
E.
Ian Hay
Ian Hay was a Scottish novelist and playwright, born John Hay Beith, known for his popular humorous and military-themed works and for adapting stories for the stage and screen.
- 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_69d6aaf84b548190ac072e4fb89ae18f |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d895502e0081909ee9c3d45d26cd91 |
completed | April 10, 2026, 6:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef8287f2dc819089b14707e035f7a1 |
completed | April 27, 2026, 3:36 p.m. |
Created at: April 8, 2026, 9:38 p.m.