Triple
T14426932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yersinia |
E357719
|
entity |
| Predicate | notableSpeciesCount |
P6211
|
FINISHED |
| Object | several pathogenic species |
—
|
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: several pathogenic species | Statement: [Yersinia, notableSpeciesCount, several pathogenic species]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableSpeciesCount Context triple: [Yersinia, notableSpeciesCount, several pathogenic species]
-
A.
notableSpecies
Indicates that the subject is known for, or significantly associated with, the specified species.
-
B.
speciesNumber
Indicates the numerical identifier or count associated with a particular species in a given context.
-
C.
numberOfSpecies
chosen
Indicates the count of distinct species associated with a given entity or context.
-
D.
notableSpeciesGroup
Indicates that an entity is a significant or characteristic member of a particular species group associated with another entity.
-
E.
commonNameOfNotableSpecies
Indicates that the subject is a commonly used vernacular or everyday name for a notable or well-known biological species.
- 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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de911398f08190be85bc0a8bef6b1b |
completed | April 14, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69de5c30467881908e770e3940295641 |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:18 a.m.