Triple
T37707399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Potion of Poison |
E939230
|
entity |
| Predicate | effectName |
P195919
|
FINISHED |
| Object | Poison |
—
|
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: Poison | Statement: [Potion of Poison, effectName, Poison]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectName Context triple: [Potion of Poison, effectName, Poison]
-
A.
effectDescription
Indicates a textual explanation of the outcome, consequence, or impact resulting from an action, event, or condition.
-
B.
effectCategory
chosen
Indicates the general type or classification of an effect that one entity has on another or on a system.
-
C.
effectCount
Indicates the number of distinct effects or outcomes associated with a given action, event, or entity.
-
D.
effectSource
Indicates that one entity is the origin or cause from which a particular effect, outcome, or influence arises for another entity.
-
E.
specialEffect
Indicates that one entity produces, applies, or is associated with a distinctive, often non-standard effect on another entity or on an event.
- 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_69f76edb49dc8190b951dce9ce6ef789 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fe5c1a502081909d4024e514309c8e |
completed | May 8, 2026, 9:56 p.m. |
| PD | Predicate disambiguation | batch_69fe5a9df21c819087153f5d0bcaa987 |
completed | May 8, 2026, 9:50 p.m. |
Created at: May 3, 2026, 4:18 p.m.