Triple
T1669552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sylvia Ashley |
E36092
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Paddington |
E78024
|
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: Paddington | Statement: [Sylvia Ashley, placeOfBirth, Paddington]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paddington Context triple: [Sylvia Ashley, placeOfBirth, Paddington]
-
A.
Paddington
chosen
Paddington is a central London district best known for its major railway station, historic canal basin, and association with the fictional Paddington Bear.
-
B.
Paddington 2
Paddington 2 is a critically acclaimed 2017 family comedy film about the beloved bear Paddington, celebrated for its warmth, humor, and inventive storytelling.
-
C.
Mr. Plod
Mr. Plod is the bumbling village policeman character from Enid Blyton’s Noddy stories, known for trying to keep order in Toyland.
-
D.
Christopher Robin
Christopher Robin is a 2018 live-action fantasy film that revisits the now-adult friend of Winnie the Pooh as he rediscovers the joys of imagination and childhood.
-
E.
Mr. Bean
Mr. Bean is a largely silent, bumbling British comedy character known for his childlike antics and visual gags in the television series and films of the same name.
- 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_69a8861286808190939afff3ce8ee31e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90adf3d3c81909233e574e79b82a2 |
completed | March 5, 2026, 4:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad683461ec8190b442054443c472b3 |
completed | March 8, 2026, 12:14 p.m. |
Created at: March 4, 2026, 7:29 p.m.