Triple
T11466614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ziehl–Neelsen stain |
E271795
|
entity |
| Predicate | visualizationMethod |
P99733
|
FINISHED |
| Object | light microscopy |
—
|
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: light microscopy | Statement: [Ziehl–Neelsen stain, visualizationMethod, light microscopy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualizationMethod Context triple: [Ziehl–Neelsen stain, visualizationMethod, light microscopy]
-
A.
visualizationLibrary
Indicates that an entity uses, depends on, or is implemented with a particular visualization library for rendering or displaying visual data.
-
B.
visualizedIn
Indicates that something is represented or depicted within a particular visual medium, view, or visualization.
-
C.
chartDepiction
Indicates that one entity is a chart that visually represents or depicts information about another entity.
-
D.
visualMetaphor
Indicates a relationship where one entity conceptually represents or explains another through a visual analogy or symbolic imagery.
-
E.
visualElements
Indicates that one entity contains, uses, or is characterized by specific visual components or graphical features associated with another entity.
- 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f5eb988190b309b8e309f6d1a5 |
completed | April 9, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69d80867ff248190bb157fa9e355353b |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:35 p.m.