Triple
T36000976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rapa Valley |
E1041128
|
entity |
| Predicate | hasSeasonalCharacteristics |
P31603
|
FINISHED |
| Object | snow cover in winter |
—
|
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: snow cover in winter | Statement: [Rapa Valley, hasSeasonalCharacteristics, snow cover in winter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeasonalCharacteristics Context triple: [Rapa Valley, hasSeasonalCharacteristics, snow cover in winter]
-
A.
hasSeasonalNature
chosen
Indicates that something exhibits characteristics, behavior, or occurrence patterns that vary according to specific seasons or times of the year.
-
B.
hasSeasonalPattern
Indicates that the occurrence, intensity, or characteristics of something regularly vary according to a recurring seasonal cycle.
-
C.
hasSeasonalStatus
Indicates that an entity’s status, availability, or condition varies according to a particular season or time of year.
-
D.
hasSeasonalHighlight
Indicates that something features a notable or emphasized aspect during a particular season or time of year.
-
E.
hasSeasonalEvents
Indicates that an entity organizes or experiences events that occur only during specific seasons or times of the year.
- 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_69f76e2a02208190aedd1f9025a8b300 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a003ffb548081908ba64fd2b570f42f |
completed | May 10, 2026, 8:21 a.m. |
| PD | Predicate disambiguation | batch_6a003fcecf788190a13dff38b9de3414 |
completed | May 10, 2026, 8:20 a.m. |
Created at: May 3, 2026, 4:07 p.m.