Triple
T21365386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berryessa Creek |
E526895
|
entity |
| Predicate | hasWatershedAreaType |
P6822
|
FINISHED |
| Object | highly urbanized |
—
|
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: highly urbanized | Statement: [Berryessa Creek, hasWatershedAreaType, highly urbanized]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWatershedAreaType Context triple: [Berryessa Creek, hasWatershedAreaType, highly urbanized]
-
A.
hasWatershed
Indicates that one geographic area or feature is part of, drains into, or is hydrologically defined by a particular watershed.
-
B.
hasWatershedWith
Indicates that two geographic areas share or are associated with the same watershed or drainage basin.
-
C.
hasWatershedCharacteristic
Indicates that a watershed possesses a specified characteristic, feature, or property.
-
D.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
E.
hasHydrologicalType
Indicates a relationship where an entity is classified according to its hydrological category or type (e.g., river, lake, aquifer).
- 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_69e0b51d8a308190b09113b3b3f9bc15 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b06e1ba081908a96928a65da5fa2 |
completed | April 22, 2026, 11:26 a.m. |
| PD | Predicate disambiguation | batch_69e6162bbfc88190a3e75859941b2638 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 5:09 p.m.