Triple
T10385899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dryden Ice Dogs |
E244759
|
entity |
| Predicate | hasHomeCity |
P5864
|
FINISHED |
| Object | Dryden |
E123216
|
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: Dryden | Statement: [Dryden Ice Dogs, hasHomeCity, Dryden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dryden Context triple: [Dryden Ice Dogs, hasHomeCity, Dryden]
-
A.
Dryden
Dryden is a small village in Tompkins County, New York, known for its rural character and proximity to the city of Ithaca.
-
B.
Dryden
chosen
Dryden is a small city in northwestern Ontario, Canada, known historically for its forestry and paper mill industries.
-
C.
Dryden
Dryden is a surname most famously associated with Ken Dryden, the Hall of Fame Canadian ice hockey goaltender and former politician.
-
D.
John Dryden
John Dryden was a leading 17th-century English poet, playwright, and critic who became the dominant literary figure of the Restoration era and the first official Poet Laureate of England.
-
E.
Mr. Dryden
Mr. Dryden is a British government official in the film "Lawrence of Arabia" who helps orchestrate T.E. Lawrence’s assignment in the Arab Revolt.
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9a42e748190b10fa7b4ca006bad |
completed | April 7, 2026, 11:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d795a30a008190a42db2eda3b5dca2 |
completed | April 9, 2026, 12:03 p.m. |
Created at: April 6, 2026, 12:04 p.m.