Triple
T1983488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aachen |
E43082
|
entity |
| Predicate | hasHotSpringsTemperature |
P4459
|
FINISHED |
| Object | up to about 74 degrees Celsius |
—
|
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: up to about 74 degrees Celsius | Statement: [Aachen, hasHotSpringsTemperature, up to about 74 degrees Celsius]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHotSpringsTemperature Context triple: [Aachen, hasHotSpringsTemperature, up to about 74 degrees Celsius]
-
A.
hasHotSpring
Indicates that one entity possesses, contains, or is associated with a hot spring.
-
B.
hasTemperature
chosen
Indicates that an entity possesses or is characterized by a specific temperature value.
-
C.
hasFreshwaterSprings
Indicates that the subject contains or is associated with natural sources of freshwater emerging from the ground.
-
D.
hasAverageSpringTemperature
Indicates that an entity is associated with a specific average temperature value measured over the spring season.
-
E.
hasSpaResort
Indicates that one entity possesses, includes, or is associated with a spa resort as an amenity or feature.
- 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_69a88713ddc88190a969715658ebe7a8 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb96f932881908bebfc4176fda7c0 |
completed | March 7, 2026, 5:36 a.m. |
| PD | Predicate disambiguation | batch_69abb798d288819083132cf14605bd02 |
completed | March 7, 2026, 5:28 a.m. |
Created at: March 4, 2026, 7:37 p.m.