Triple
T14053817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam De Grasse |
E338159
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | De Grasse |
E338159
|
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: De Grasse | Statement: [Sam De Grasse, hasSurname, De Grasse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: De Grasse Context triple: [Sam De Grasse, hasSurname, De Grasse]
-
A.
De Grasse
chosen
De Grasse is a surname most notably associated with early 20th-century Canadian-born silent film actor Sam De Grasse.
-
B.
Guy Marchand
Guy Marchand is a French actor, singer, and musician known for his prolific film and television career, including notable roles in French cinema classics.
-
C.
Raleigh Bertrand
Raleigh Bertrand is a member of the Bertrand family, related to the late actress and humanitarian Marcheline Bertrand.
-
D.
Patrick Jean
Patrick Jean is a French filmmaker and visual effects artist best known for creating the short film that inspired the feature-length movie "Pixels."
-
E.
William Scott Bowman
William Scott Bowman, better known as Scotty Bowman, is a legendary Canadian ice hockey coach widely regarded as one of the greatest and most successful coaches in NHL history.
- 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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de3c8bc54c8190a12f0fc056568538 |
completed | April 14, 2026, 1:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcb65e63b48190bd5bd4a36cd15396 |
completed | May 7, 2026, 3:57 p.m. |
Created at: April 9, 2026, 10:20 p.m.