Triple
T35968009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akshobhya |
E1040196
|
entity |
| Predicate | transformsPoison |
P200031
|
FINISHED |
| Object | anger |
—
|
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: anger | Statement: [Akshobhya, transformsPoison, anger]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transformsPoison Context triple: [Akshobhya, transformsPoison, anger]
-
A.
transformsUnder
Indicates a relationship where one entity changes form, state, or structure when subjected to the influence, conditions, or operation specified by another entity.
-
B.
moreResistantToPoisoningThan
Indicates that one entity has a higher resistance or tolerance to poisoning than another entity.
-
C.
poisonUsed
Indicates that one entity employed poison as a means to harm, kill, or incapacitate another entity.
-
D.
takesFormOf
Indicates that one entity assumes, manifests, or is expressed in the shape, structure, or configuration of another entity.
-
E.
transformsCharacterInto
Indicates that one entity causes or undergoes a change that turns a character into another form, state, or identity.
- 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_69f76e26b21081909fd9ffb3aff6c77a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff6c061c6c81909ff485e9cafc88a2 |
completed | May 9, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69ff6aaf886c8190a3c87d089453f3de |
completed | May 9, 2026, 5:11 p.m. |
| PDg | Predicate description generation | batch_69ff6c04fa208190b1fab40a71ef923f |
completed | May 9, 2026, 5:16 p.m. |
Created at: May 3, 2026, 4:07 p.m.