Triple
T2523532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CB1 receptor |
E55578
|
entity |
| Predicate | hasTransmembraneDomains |
P39646
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [CB1 receptor, hasTransmembraneDomains, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTransmembraneDomains Context triple: [CB1 receptor, hasTransmembraneDomains, 7]
-
A.
hasProteinBinding
Indicates that one entity is capable of physically binding to or interacting specifically with a protein.
-
B.
hasMolecularTarget
Indicates that one entity (such as a drug or compound) is directed toward, binds to, or specifically interacts with a particular molecular target (such as a protein, receptor, or gene).
-
C.
hasPhylum
Indicates that an entity (typically a biological taxon or organism) is classified as belonging to a particular phylum in a taxonomic hierarchy.
-
D.
hasTissue
Indicates that one entity possesses, contains, or is associated with a specific tissue of another entity.
-
E.
membraneLipidType
Indicates the specific type or class of lipid that composes or is associated with a membrane.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd23a0a548190b44393e0f823f7a9 |
completed | March 7, 2026, 7:22 a.m. |
| PD | Predicate disambiguation | batch_69abd0c144b0819092f32a13c1d127e5 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd1487e0c8190b90dcf30586ad4cd |
completed | March 7, 2026, 7:18 a.m. |
Created at: March 6, 2026, 9:46 p.m.