Triple
T22692333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SMN1 |
E561082
|
entity |
| Predicate | exonCountApproximate |
P149301
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [SMN1, exonCountApproximate, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exonCountApproximate Context triple: [SMN1, exonCountApproximate, 9]
-
A.
estimatedNumberOfGenes
Indicates the approximate count of genes that an entity (such as an organism or genome) is believed to possess.
-
B.
sectionCountApproximate
Indicates that the number of sections associated with an entity is known only approximately rather than as an exact count.
-
C.
genomeSegmentCount
Indicates the number of distinct genomic segments associated with a given biological entity or sample.
-
D.
genomeSize
Indicates the total amount of genetic material (e.g., in base pairs) contained in an organism’s genome.
-
E.
isoformNumber
Indicates the specific variant index or identifier assigned to one isoform among multiple alternative forms of the same gene or protein.
- 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_69e2454d71b48190a1f80af9f82b6fcf |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1789ba0148190891781d05ec64f3c |
completed | April 29, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69ee62b2259c819091ed1387a748b9f3 |
completed | April 26, 2026, 7:08 p.m. |
| PDg | Predicate description generation | batch_69ee8843d3308190b6e22bb98ae5c3d8 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 3:13 p.m.