Triple
T20762933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greater Toronto Area |
E511016
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Ajax |
—
|
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: Ajax | Statement: [Greater Toronto Area, hasCity, Ajax]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ajax Context triple: [Greater Toronto Area, hasCity, Ajax]
-
A.
Ajax
Ajax is a charismatic, free-spirited drifter in Toni Morrison’s novel "Sula," whose passionate but ultimately transient relationship with Sula Peace highlights themes of independence and emotional detachment.
-
B.
Ajax
Ajax is a long-standing household cleaning product brand known for its powdered and liquid cleaners, owned by Colgate-Palmolive.
-
C.
Ajax
Ajax is a modern British Army armoured fighting vehicle designed for advanced reconnaissance, surveillance, and battlefield intelligence roles.
-
D.
Ajax
Ajax is a sadistic mutant villain from Marvel's Deadpool comics and film, known for torturing Wade Wilson and inadvertently creating Deadpool.
-
E.
Ajax
chosen
Ajax is a suburban town in the eastern part of the Greater Toronto Area in Ontario, Canada, known for its residential communities and growing commercial development along Lake Ontario.
- 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_69e0b4c909ec8190b05987f1639513f6 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c24a154c8190a9062923308d2411 |
completed | April 21, 2026, 12:18 a.m. |
Created at: April 16, 2026, 12:35 p.m.