Triple
T2209095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | the Screaming Sixties |
E50870
|
entity |
| Predicate | hasMeteorologicalFeature |
P26038
|
FINISHED |
| Object | deep low-pressure systems |
—
|
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: deep low-pressure systems | Statement: [the Screaming Sixties, hasMeteorologicalFeature, deep low-pressure systems]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMeteorologicalFeature Context triple: [the Screaming Sixties, hasMeteorologicalFeature, deep low-pressure systems]
-
A.
hasMeteorologicalStation
Indicates that one entity possesses, hosts, or is equipped with a meteorological station used for observing and recording weather-related data.
-
B.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
C.
hasAtmosphericFeature
chosen
Indicates that one entity possesses or exhibits a particular feature or characteristic of its atmosphere.
-
D.
hasWeather
Indicates that a location or environment is experiencing or characterized by a particular type of weather condition.
-
E.
hasNaturalFeature
Indicates that one entity possesses, contains, or is characterized by a particular natural feature (such as a mountain, river, forest, or coastline).
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1baa0948190b07ffc347a4f714e |
completed | March 7, 2026, 6:12 a.m. |
| PD | Predicate disambiguation | batch_69abbda8a6dc8190aa855ce2d17194b1 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:46 p.m.