Triple
T31257866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shui Xian |
E797029
|
entity |
| Predicate | temperatureRecommendation |
P171508
|
FINISHED |
| Object | near-boiling water |
—
|
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: near-boiling water | Statement: [Shui Xian, temperatureRecommendation, near-boiling water]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temperatureRecommendation Context triple: [Shui Xian, temperatureRecommendation, near-boiling water]
-
A.
temperatureConditions
Indicates the specific thermal or weather-related temperature state or range affecting an entity or situation.
-
B.
typicalTemperature
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
-
C.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
D.
temperatureRangeVariant
Indicates a relationship where one temperature range is a variation or alternative form of another temperature range.
-
E.
dressRecommendation
Indicates a suggested or advised choice of dress for a particular person and/or occasion.
- F. None of above. chosen
Provenance (4 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_69f224dd5fdc81908a4cd24917b67668 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69f80b62c8190bf2af2be0d3a7df8 |
completed | May 3, 2026, 1:06 a.m. |
| PD | Predicate disambiguation | batch_69f69d1bf8cc8190a78dfa5ab00daf3a |
completed | May 3, 2026, 12:55 a.m. |
| PDg | Predicate description generation | batch_69f69edae2448190925ce701c8792c52 |
completed | May 3, 2026, 1:03 a.m. |
Created at: April 29, 2026, 9:12 p.m.