Triple
T34400409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Empress of Canada (2029) |
E882961
|
entity |
| Predicate | hasNumberOfFunnels |
P45303
|
FINISHED |
| Object | to be determined |
—
|
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: to be determined | Statement: [SS Empress of Canada (2029), hasNumberOfFunnels, to be determined]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfFunnels Context triple: [SS Empress of Canada (2029), hasNumberOfFunnels, to be determined]
-
A.
numberOfFunnels
chosen
Indicates the quantity of funnels associated with or present on a given entity.
-
B.
hasFunnels
Indicates that one entity possesses or is equipped with one or more funnels associated with another entity or context.
-
C.
hasFunnelType
Indicates that an entity is associated with or characterized by a specific type or category of funnel.
-
D.
hasNumberOfConvergingAvenues
Indicates the number of distinct avenues that meet or converge at a particular location or junction.
-
E.
hasNumberOfDrops
Indicates the quantity or count of drops associated with an entity or event.
- 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_69f349c1304081909331872829e38106 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fea1f5d8c481908dc3351dc9ecef7f |
completed | May 9, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69fea06b6fe0819095bf4c1bc9809927 |
completed | May 9, 2026, 2:48 a.m. |
Created at: May 1, 2026, 1:59 a.m.