Triple
T25111252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Escherichia coli |
E628999
|
entity |
| Predicate | modelIn |
P158185
|
FINISHED |
| Object | microbiology |
—
|
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: microbiology | Statement: [Escherichia coli, modelIn, microbiology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modelIn Context triple: [Escherichia coli, modelIn, microbiology]
-
A.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
B.
possibleModel
Indicates that one entity can serve as a potential or candidate model or template for another entity.
-
C.
modeledWith
Indicates that something is represented, simulated, or described using a particular model, method, or modeling technique.
-
D.
hadModel
Indicates that an entity possessed, used, or was associated with a particular model (e.g., a product, design, or version) at some point in time.
-
E.
manufacturingModel
Indicates that one entity serves as the manufacturing model, design, or template according to which another entity is produced or manufactured.
- 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_69e2ff3169d08190973b6061d5009abd |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f465767c48819086fa0573dca12276 |
completed | May 1, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69f45cfb53f4819099bba48c5057e787 |
completed | May 1, 2026, 7:57 a.m. |
| PDg | Predicate description generation | batch_69f464ae42e88190b3549fdf4e0b425e |
completed | May 1, 2026, 8:30 a.m. |
Created at: April 18, 2026, 6:27 a.m.