Triple
T19623153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donald Sangster |
E471064
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Sangster |
—
|
NE NERFINISHED |
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: Sangster | Statement: [Donald Sangster, familyName, Sangster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sangster Context triple: [Donald Sangster, familyName, Sangster]
-
A.
Sangster
chosen
Sangster is the surname of English actor Thomas Brodie-Sangster, known for roles in films like "Love Actually" and series such as "Game of Thrones" and "The Queen's Gambit."
-
B.
Carricart
Carricart is a Spanish-language surname of likely Basque origin borne by individuals such as María del Carmen Cerruti Carricart.
-
C.
Morgado
Morgado is a Portuguese surname most notably borne by actor Diogo Morgado, known for his roles in film and television.
-
D.
Gonsalves
Gonsalves is a Portuguese-origin surname commonly found in Lusophone and Caribbean communities.
-
E.
Abataranika
Abataranika is a Bengali literary work that served as the source material for the film "Mahanagar."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e510fa248190b7afb274a1d4cf73 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640e7949081908a89414ef899fa20 |
completed | April 20, 2026, 3:06 p.m. |
Created at: April 10, 2026, 1:44 p.m.