Triple
T21537446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assomption |
E531384
|
entity |
| Predicate | rankByDepthInMontrealMetro |
P141712
|
FINISHED |
| Object | 37 |
—
|
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: 37 | Statement: [Assomption, rankByDepthInMontrealMetro, 37]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankByDepthInMontrealMetro Context triple: [Assomption, rankByDepthInMontrealMetro, 37]
-
A.
depthRankInMontrealMetro
chosen
Indicates the relative ordering of how deep a metro station or segment is located below ground within the Montreal Metro system.
-
B.
rankByDepthInNYCSubway
Indicates the relative ordering of entities based on how deep they are located within the New York City subway system.
-
C.
distanceFromQuebecCityCentre
Indicates the measured spatial distance between a given location and the center of Quebec City.
-
D.
distanceToMontreal
Indicates the spatial distance between a given entity’s location and the city of Montreal.
-
E.
distanceFromParisSaintLazare
Indicates the physical distance between a given place and Paris Saint-Lazare railway station.
- 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_69e0c45e5b8881908ac18fc2f493b114 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee9d0fdf448190b47ac7c28904f86b |
completed | April 26, 2026, 11:17 p.m. |
| PD | Predicate disambiguation | batch_69e6320766308190ba5dca2f7c826aa4 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:27 p.m.