Triple
T19695160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Town, Illinois |
E472936
|
entity |
| Predicate | associatedWithSeasonalImagery |
P31603
|
FINISHED |
| Object | summer |
—
|
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: summer | Statement: [Green Town, Illinois, associatedWithSeasonalImagery, summer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithSeasonalImagery Context triple: [Green Town, Illinois, associatedWithSeasonalImagery, summer]
-
A.
hasSeasonalNature
chosen
Indicates that something exhibits characteristics, behavior, or occurrence patterns that vary according to specific seasons or times of the year.
-
B.
affectedSeason
Indicates that one entity has an influence on, or causes a change in, a particular season.
-
C.
usesImageryOf
Indicates that one entity employs or incorporates visual or sensory imagery that depicts, references, or symbolically represents another entity.
-
D.
basedOnSeason
Indicates that something is determined, influenced, or derived according to a particular season or time of year.
-
E.
associatedWithWeather
Indicates a relationship where something is connected or related to weather conditions or phenomena.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e642147c3c8190a4c788e4bb48fe11 |
completed | April 20, 2026, 3:11 p.m. |
| PD | Predicate disambiguation | batch_69e53039ea808190a9106a53f564ab92 |
completed | April 19, 2026, 7:42 p.m. |
Created at: April 10, 2026, 1:46 p.m.