Triple
T15818427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Voyageur II |
E383537
|
entity |
| Predicate | serviceSeason |
P120196
|
FINISHED |
| Object | seasonal (not year-round) |
—
|
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: seasonal (not year-round) | Statement: [Voyageur II, serviceSeason, seasonal (not year-round)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serviceSeason Context triple: [Voyageur II, serviceSeason, seasonal (not year-round)]
-
A.
sportSeasonOf
Indicates that one entity is a sports season that belongs to, or is part of, the overall history or schedule of the specified sport.
-
B.
trainingSeason
Indicates the specific season or time period during which training activities or programs take place.
-
C.
promotionSeason
Indicates the specific season or time period during which a promotion or promotional campaign is active.
-
D.
hasSeasonType
Indicates that something is associated with a particular category or type of season (e.g., summer, winter, rainy).
-
E.
hasSeasonalRound
Indicates a recurring, seasonally patterned cycle of movements, activities, or resource use associated with an entity over the course of a year.
- 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_69d86da2858c819090cc8481e7207b6e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0c4a552008190863343c9d41ebf3f |
completed | April 16, 2026, 11:14 a.m. |
| PD | Predicate disambiguation | batch_69e0053b847c8190945726c3ddac21cc |
completed | April 15, 2026, 9:38 p.m. |
| PDg | Predicate description generation | batch_69e00e48d49c819081afccb02f9cf18b |
completed | April 15, 2026, 10:16 p.m. |
Created at: April 10, 2026, 4:49 a.m.