Triple
T2366496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CLARITY tissue-clearing method |
E47391
|
entity |
| Predicate | renders |
P37806
|
FINISHED |
| Object | biological tissues optically transparent |
—
|
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: biological tissues optically transparent | Statement: [CLARITY tissue-clearing method, renders, biological tissues optically transparent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: renders Context triple: [CLARITY tissue-clearing method, renders, biological tissues optically transparent]
-
A.
renderedBy
Indicates that something is produced, drawn, or visually generated by a particular agent, tool, or process.
-
B.
renderedUnder
Indicates that one entity was produced, displayed, or executed within the authority, context, or environment provided by another entity.
-
C.
displays
Indicates that one entity visually presents or shows another entity’s content or information.
-
D.
representation
Indicates that one entity stands in for, symbolizes, or depicts another entity in some context.
-
E.
notableRendering
Indicates that one entity is a significant or well-known visual or artistic depiction of 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_69a88a1a4a6081908645b0f2914521ab |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc749f8e0819094144b9dd9db8790 |
completed | March 7, 2026, 6:35 a.m. |
| PD | Predicate disambiguation | batch_69abc59b88348190a2d6c08f69974117 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abc6443c6c8190b932de2abd8eb28f |
completed | March 7, 2026, 6:31 a.m. |
Created at: March 4, 2026, 7:55 p.m.