Triple
T30086787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betz cells |
E764621
|
entity |
| Predicate | hasSomaDiameter |
P7302
|
FINISHED |
| Object | up to about 100 micrometers |
—
|
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: up to about 100 micrometers | Statement: [Betz cells, hasSomaDiameter, up to about 100 micrometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSomaDiameter Context triple: [Betz cells, hasSomaDiameter, up to about 100 micrometers]
-
A.
hasDiameterClass
Indicates that an entity is associated with a specific category or range based on the size of its diameter.
-
B.
bodyDiameter
Indicates the measurement of how wide an object's body is across its broadest cross-section.
-
C.
hasShoulderDiameter
Indicates the diameter measurement of an entity’s shoulder region or shoulder-related feature.
-
D.
shellDiameter
Indicates the diameter measurement of a shell, typically specifying the distance across it at its widest point.
-
E.
approximateDiameter
chosen
Indicates that one entity specifies the estimated or rough measurement of another entity’s diameter.
- 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_69f22473c0fc8190a926a8051b3b378b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7626667f48190ad90867eb67ec582 |
completed | May 3, 2026, 2:57 p.m. |
| PD | Predicate disambiguation | batch_69f76175d6608190b60b268e20f49ed9 |
completed | May 3, 2026, 2:53 p.m. |
Created at: April 29, 2026, 7:04 p.m.