Triple
T3379584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olympic Games Marathon |
E71148
|
entity |
| Predicate | weatherImpact |
P32056
|
FINISHED |
| Object | race conditions strongly affected by heat and humidity |
—
|
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: race conditions strongly affected by heat and humidity | Statement: [Olympic Games Marathon, weatherImpact, race conditions strongly affected by heat and humidity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: weatherImpact Context triple: [Olympic Games Marathon, weatherImpact, race conditions strongly affected by heat and humidity]
-
A.
climateChangeEffect
Indicates how climate change influences or alters a particular entity, condition, or process.
-
B.
humanImpact
Indicates the effect or influence that human activities have on another entity, system, or environment.
-
C.
seasonImpact
Indicates how a particular season influences or affects another entity, condition, or outcome.
-
D.
hasClimateInfluence
Indicates that one entity affects or contributes to the climate characteristics or climate-related conditions of another entity.
-
E.
associatedWithWeather
chosen
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_69ad85a7f80c8190a05e43013f298942 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb2ec38d88190be8c824daeca5ab6 |
completed | March 8, 2026, 5:33 p.m. |
| PD | Predicate disambiguation | batch_69ada434bae48190a77ea37f9274ad8f |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:14 p.m.