Triple
T37449660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bacteria |
E930642
|
entity |
| Predicate | hasCellularity |
P193032
|
FINISHED |
| Object | Unicellular |
—
|
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: Unicellular | Statement: [Bacteria, hasCellularity, Unicellular]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCellularity Context triple: [Bacteria, hasCellularity, Unicellular]
-
A.
hasCellas
Indicates that one entity possesses or contains a cellar as part of its structure or property.
-
B.
numberOfCells
Indicates the total count of individual cells associated with or contained in a given entity.
-
C.
cellLevels
Indicates a relationship where specific levels or layers within a cell (or cellular structure) are identified, characterized, or associated with another entity.
-
D.
hasCellularComponent
Indicates that an entity possesses, includes, or is associated with a specific cellular component as part of its structure or organization.
-
E.
hadCellStructure
Indicates that an entity possessed a particular cellular organization or arrangement of cells.
- 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_69f76ec0b9488190b7a4fae632bd1d2f |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd35d108908190b79b1e8e6bbd62aa |
completed | May 8, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69fd34cb46108190b43c3b7f67ec4cd4 |
completed | May 8, 2026, 12:56 a.m. |
| PDg | Predicate description generation | batch_69fd35d029588190a525aa8a506e7708 |
completed | May 8, 2026, 1:01 a.m. |
Created at: May 3, 2026, 4:17 p.m.