Triple
T9830988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puente del Inca |
E238979
|
entity |
| Predicate | hotSpringsTemperature |
P9307
|
FINISHED |
| Object | approximately 28–34 °C |
—
|
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: approximately 28–34 °C | Statement: [Puente del Inca, hotSpringsTemperature, approximately 28–34 °C]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hotSpringsTemperature Context triple: [Puente del Inca, hotSpringsTemperature, approximately 28–34 °C]
-
A.
hasNumberOfHotSprings
Indicates the quantity of hot springs associated with a given entity.
-
B.
hasHotSpring
Indicates that one entity possesses, contains, or is associated with a hot spring.
-
C.
hasMineralSprings
Indicates that a place or entity possesses or is characterized by the presence of mineral springs.
-
D.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
E.
waterTemperatureType
chosen
Indicates the classification or category of a water body’s temperature (e.g., cold, warm, hot) associated with an entity or context.
- 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_69ca84e314108190978324a4bdb959f8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3297bd88190bf8c53a4ba00e0ae |
completed | April 2, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69cd03e30bc08190816c0a6d29c21b0f |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:32 p.m.