Triple
T9531998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tinca tinca |
E229917
|
entity |
| Predicate | turbidityTolerance |
P88574
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Tinca tinca, turbidityTolerance, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: turbidityTolerance Context triple: [Tinca tinca, turbidityTolerance, high]
-
A.
hasWaterClarity
Indicates the degree to which water in a given context is clear, transparent, or free from visible impurities.
-
B.
waterTolerance
Indicates the degree to which an entity can withstand or function effectively in the presence of water.
-
C.
hasSedimentLoad
Indicates that one entity (typically a water body or flow) carries or transports a certain amount or type of sediment associated with another entity.
-
D.
saltTolerance
Indicates the degree to which an entity can withstand or function under saline (high-salt) conditions.
-
E.
typeOfSedimentation
Indicates the specific kind or process of sediment deposition or settling that characterizes how sediment accumulates in a given context.
- 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_69ca8479934c81908006d0e6e970ae05 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98b408648190a04127c1d47fe7d2 |
completed | April 1, 2026, 10:14 p.m. |
| PD | Predicate disambiguation | batch_69cca56c44f88190a54a5d2a133bb07e |
completed | April 1, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69cca89f1d748190bf3636bea28d8a37 |
completed | April 1, 2026, 5:09 a.m. |
Created at: March 30, 2026, 8 p.m.