Triple
T37769107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Potion of Regeneration |
E941491
|
entity |
| Predicate | effectMechanic |
P195919
|
FINISHED |
| Object | gradual health restoration |
—
|
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: gradual health restoration | Statement: [Potion of Regeneration, effectMechanic, gradual health restoration]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectMechanic Context triple: [Potion of Regeneration, effectMechanic, gradual health restoration]
-
A.
effectTrigger
Indicates that one event, condition, or action initiates or causes another effect to occur.
-
B.
effectDescription
Indicates a textual explanation of the outcome, consequence, or impact resulting from an action, event, or condition.
-
C.
effectCategory
chosen
Indicates the general type or classification of an effect that one entity has on another or on a system.
-
D.
effectSource
Indicates that one entity is the origin or cause from which a particular effect, outcome, or influence arises for another entity.
-
E.
effectCount
Indicates the number of distinct effects or outcomes associated with a given action, event, or 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_69f76ee3251881909bb4451aad50752b |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fe5d58c3e48190910aa3c23485e2c4 |
completed | May 8, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69fe5c92090c8190bcfa412c0a3619df |
completed | May 8, 2026, 9:58 p.m. |
Created at: May 3, 2026, 4:19 p.m.