Triple
T5040978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fairy Pools |
E113542
|
entity |
| Predicate | waterAppearance |
P60967
|
FINISHED |
| Object | vividly blue |
—
|
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: vividly blue | Statement: [Fairy Pools, waterAppearance, vividly blue]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterAppearance Context triple: [Fairy Pools, waterAppearance, vividly blue]
-
A.
waterType
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
-
B.
waterQualityIssues
Indicates that there are problems or concerns with the condition, safety, or suitability of a water source.
-
C.
waterCondition
Indicates the state or quality of water affecting an entity, such as its cleanliness, safety, or suitability for a particular use.
-
D.
waterQualityUse
Indicates the way in which water quality is evaluated, classified, or applied for specific purposes or uses.
-
E.
hasWaterClarity
Indicates the degree to which water in a given context is clear, transparent, or free from visible impurities.
- 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_69bd44384298819089c49e7c330ec7b8 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73de73008190b89aec9a76b43e4f |
completed | March 20, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69bd71529d608190a53470ba6c14bb1d |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd73617f348190b2fa68a0ef4fc7b1 |
completed | March 20, 2026, 4:18 p.m. |
Created at: March 20, 2026, 1:37 p.m.