Triple
T37707362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Potion of Poison |
E939230
|
entity |
| Predicate | appliesStatusEffectTo |
P100810
|
FINISHED |
| Object | most living entities |
—
|
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: most living entities | Statement: [Potion of Poison, appliesStatusEffectTo, most living entities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesStatusEffectTo Context triple: [Potion of Poison, appliesStatusEffectTo, most living entities]
-
A.
hasStatusEffectSource
Indicates that a status effect currently applied to an entity originates from, or was caused by, a specific source entity or event.
-
B.
hasEffectIn
Indicates that one entity produces, causes, or exerts an effect within a specified context, system, or environment.
-
C.
grantsStatusEffect
chosen
Indicates that one entity applies or bestows a specific status effect onto another entity.
-
D.
providesEffect
Indicates that one entity causes, delivers, or produces a particular effect or outcome on another entity.
-
E.
attackEffect
Indicates that one entity’s attack produces a specific effect or consequence on 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_69f76edb49dc8190b951dce9ce6ef789 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ffbb1c5bf88190a0bf791213045885 |
completed | May 9, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69ffba0ab0f881908f84ef81f7a1bfe8 |
completed | May 9, 2026, 10:49 p.m. |
Created at: May 3, 2026, 4:18 p.m.