Triple
T6073397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samuel of Bulgaria |
E135339
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Agatha |
E95480
|
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: Agatha | Statement: [Samuel of Bulgaria, spouse, Agatha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Agatha Context triple: [Samuel of Bulgaria, spouse, Agatha]
-
A.
Agatha
Agatha is a young pastry chef at Mendl’s who becomes a key ally and love interest in Wes Anderson’s film "The Grand Budapest Hotel."
-
B.
Agatha
Agatha is a precognitive woman in the science fiction film "Minority Report" whose visions of future crimes are central to the story's plot and moral conflict.
-
C.
Agatha
Agatha is a character from the horror film "Night Monster," likely involved in the film’s eerie and suspenseful events.
-
D.
Agatha
chosen
Agatha was an 11th-century noblewoman, likely of Eastern European or possibly Hungarian or Kievan Rus' origin, best known as the mother of Edgar the Ætheling and Saint Margaret of Scotland.
-
E.
Aunt Agatha
Aunt Agatha is a formidable, domineering aunt in P. G. Wodehouse’s Jeeves and Wooster stories, notorious for terrorizing her nephew Bertie Wooster and trying to manage his life.
- 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_69c00879e8048190b690717d19c5bc03 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0575b9bc08190a78b3082b9ccf00c |
completed | March 22, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d3a37fc81909bbc1cdeec3205cf |
completed | March 23, 2026, 11 a.m. |
Created at: March 22, 2026, 4:11 p.m.