Triple
T34800647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto–Montreal |
E1003208
|
entity |
| Predicate | connectsLargestCityOfCountry |
P94566
|
FINISHED |
| Object | Toronto |
—
|
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: Toronto | Statement: [Toronto–Montreal, connectsLargestCityOfCountry, Toronto]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsLargestCityOfCountry Context triple: [Toronto–Montreal, connectsLargestCityOfCountry, Toronto]
-
A.
countryLargestCityOfSee
Indicates that a country is associated with the largest city of a specified administrative or geographic entity (such as a region, state, or territory).
-
B.
majorCityInCountry
Indicates that a city is a primary or significant urban center within a specified country.
-
C.
hasCountryLargestCity
Indicates that a country has, as its largest city, the specified city.
-
D.
connectsLargestCitiesOf
chosen
Indicates a relationship where something (typically a route, network, or infrastructure) links together the largest cities within a specified region or set.
-
E.
largestCity
Indicates that one city is the most populous or significant urban center within a specified region or entity.
- F. None of above.
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_69f76db543808190b188c6c86a91491b |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69ff6a4ce9a08190b98abde3a170dd69 |
completed | May 9, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69ff69c11634819089d1084bd2c11534 |
completed | May 9, 2026, 5:07 p.m. |
Created at: May 3, 2026, 3:59 p.m.