Triple
T1219110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tahoe Vista |
E26176
|
entity |
| Predicate | hasSeasonalCharacteristic |
P10789
|
FINISHED |
| Object | cold snowy winters |
—
|
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: cold snowy winters | Statement: [Tahoe Vista, hasSeasonalCharacteristic, cold snowy winters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeasonalCharacteristic Context triple: [Tahoe Vista, hasSeasonalCharacteristic, cold snowy winters]
-
A.
hasSeasonalStatus
Indicates that an entity’s status, availability, or condition varies according to a particular season or time of year.
-
B.
hasSeasonalPattern
Indicates that the occurrence, intensity, or characteristics of something regularly vary according to a recurring seasonal cycle.
-
C.
hasSeason
Indicates that an entity possesses, occurs during, or is associated with a particular season or set of seasons.
-
D.
seasonCharacterization
Indicates how a particular season is described, defined, or characterized in terms of its qualities or attributes.
-
E.
winterCharacteristic
chosen
Indicates a characteristic, feature, or quality that is specifically associated with or typical of winter.
- 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_69a4948331fc8190b531ac9bec71c491 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be1d55a08190a138b2411a7c4376 |
completed | March 1, 2026, 10:30 p.m. |
| PD | Predicate disambiguation | batch_69a4bb62a7c08190a79dcb6ff72ac99b |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:46 p.m.