Triple
T25111718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | bacteriophage lambda |
E629012
|
entity |
| Predicate | hasGCContent |
P11987
|
FINISHED |
| Object | approximately 50 percent |
—
|
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: approximately 50 percent | Statement: [bacteriophage lambda, hasGCContent, approximately 50 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGCContent Context triple: [bacteriophage lambda, hasGCContent, approximately 50 percent]
-
A.
hasGene
Indicates that an entity possesses or contains a specific gene as part of its genetic makeup.
-
B.
hasGenomeStructure
Indicates that one entity possesses or is characterized by a specific genome organization or arrangement.
-
C.
hasNitrogenousBase
Indicates that one entity possesses or is associated with a specific nitrogenous base as a component or characteristic.
-
D.
GCContent
chosen
Indicates the proportion of guanine (G) and cytosine (C) bases relative to the total nucleotide content in a DNA or RNA sequence.
-
E.
hasGenomeType
Indicates that an entity possesses or is characterized by a specific type or classification of genome.
- 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_69e2ff3169d08190973b6061d5009abd |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f46577989081909278965a9844ebad |
completed | May 1, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69f442c861188190967655c6d8012380 |
completed | May 1, 2026, 6:06 a.m. |
Created at: April 18, 2026, 6:27 a.m.