Triple
T20394885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sykes Hot Springs area |
E500174
|
entity |
| Predicate | waterTreatmentRecommended |
P139968
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Sykes Hot Springs area, waterTreatmentRecommended, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterTreatmentRecommended Context triple: [Sykes Hot Springs area, waterTreatmentRecommended, true]
-
A.
usesWaterTreatment
Indicates that an entity applies or relies on a water treatment process or system in its operations or activities.
-
B.
waterTreatedAt
Indicates that water has undergone treatment or purification at a specified facility or location.
-
C.
waterQualityUse
Indicates the way in which water quality is evaluated, classified, or applied for specific purposes or uses.
-
D.
waterType
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
-
E.
waterQualityIssues
Indicates that there are problems or concerns with the condition, safety, or suitability of a water source.
- 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e679122058819083e3615a5ea0a82e |
completed | April 20, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69e5765d7cb48190adec18d6d1e3d263 |
completed | April 20, 2026, 12:42 a.m. |
| PDg | Predicate description generation | batch_69e58d7481508190a87c8b88f9df9879 |
completed | April 20, 2026, 2:20 a.m. |
Created at: April 16, 2026, 11:28 a.m.