Triple
T11057957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scale tissue clearing method |
E261426
|
entity |
| Predicate | effectOnTissue |
P8792
|
FINISHED |
| Object | swelling of tissue |
—
|
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: swelling of tissue | Statement: [Scale tissue clearing method, effectOnTissue, swelling of tissue]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnTissue Context triple: [Scale tissue clearing method, effectOnTissue, swelling of tissue]
-
A.
infectsTissue
Indicates that one entity (typically a pathogen or agent) invades and establishes itself within the tissue of another entity.
-
B.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
-
C.
involvedPhysicalEffect
chosen
Indicates that one entity participates in causing, experiencing, or mediating a physical effect on another entity or the environment.
-
D.
hasTissue
Indicates that one entity possesses, contains, or is associated with a specific tissue of another entity.
-
E.
effectOnUser
Indicates how an action, event, or condition influences or impacts a user.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798a2efa48190b290f43dfe836501 |
completed | April 9, 2026, 12:16 p.m. |
| PD | Predicate disambiguation | batch_69d7440da46c8190a77380d5d747ac9c |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:26 p.m.