Triple
T20926517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LaSalle station |
E515358
|
entity |
| Predicate | depthRankInMontrealMetro |
P141712
|
FINISHED |
| Object | 52 |
—
|
LITERAL 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: 52 | Statement: [LaSalle station, depthRankInMontrealMetro, 52]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depthRankInMontrealMetro Context triple: [LaSalle station, depthRankInMontrealMetro, 52]
-
A.
rankByDepthInNYCSubway
Indicates the relative ordering of entities based on how deep they are located within the New York City subway system.
-
B.
populationRankInQuebec
Indicates the relative position of an entity in terms of population size compared to other entities within Quebec.
-
C.
metropolitanRank
Indicates the relative standing or position of a metropolitan area within a ranked ordering of metropolitan regions.
-
D.
distanceFromQuebecCityCentre
Indicates the measured spatial distance between a given location and the center of Quebec City.
-
E.
distanceToMontreal
Indicates the spatial distance between a given entity’s location and the city of Montreal.
- F. None of above. chosen
Provenance (4 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_69e0b4fb431c8190b9d40e6a72f0cc87 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6f65200b08190ac208204a20f5a6a |
completed | April 21, 2026, 4 a.m. |
| PD | Predicate disambiguation | batch_69e5c9af1fe08190953366a466950140 |
completed | April 20, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e5d53d22d08190bc17ed4bed53804a |
completed | April 20, 2026, 7:26 a.m. |
Created at: April 16, 2026, 12:49 p.m.