Triple
T625540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Weinstein Company |
E15809
|
entity |
| Predicate | notableWork |
P4
|
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: [The Weinstein Company, notableWork, Paddington]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paddington Context triple: [The Weinstein Company, notableWork, 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.
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.
-
C.
Horton
Horton is the middle name of the influential British mathematician John H. Conway, renowned for his work in group theory, knot theory, and recreational mathematics.
-
D.
Maurice
Maurice is a masculine given name of Latin origin, commonly used in English and French-speaking countries.
-
E.
Curious George
Curious George is a classic children's book and animated television character, a mischievous little monkey whose curious adventures teach gentle lessons to young audiences.
- 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_69a4935c131c8190a5378c6bf101e8cc |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e574444819087999404f3e3ffd9 |
completed | March 1, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5670380948190954bbdf802ed403c |
completed | March 2, 2026, 10:31 a.m. |
Created at: March 1, 2026, 7:35 p.m.