Triple
T5689100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CLARITY AD |
E125383
|
entity |
| Predicate | blinding |
P22656
|
FINISHED |
| Object | double-blind |
—
|
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: double-blind | Statement: [CLARITY AD, blinding, double-blind]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: blinding Context triple: [CLARITY AD, blinding, double-blind]
-
A.
blinded
Indicates that one entity causes another to lose the ability to see, either temporarily or permanently.
-
B.
blindedBy
chosen
Indicates that one entity causes another to lose the ability to see or perceive clearly, either literally or metaphorically.
-
C.
binding
Indicates that one entity physically or chemically attaches, adheres, or forms a stable association with another entity.
-
D.
tricked
Indicates that one entity intentionally deceived another into believing something false or acting under a false impression.
-
E.
correctsAberration
Indicates that one entity counteracts, fixes, or compensates for an error, flaw, or deviation present in another entity.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c0e0408190ab6c3cd3f907e80f |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:44 p.m.