Triple
T7187625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fire Flower |
E167609
|
entity |
| Predicate | effectOnUser |
P75607
|
FINISHED |
| Object | changes outfit color to white and red for Mario in many games |
—
|
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: changes outfit color to white and red for Mario in many games | Statement: [Fire Flower, effectOnUser, changes outfit color to white and red for Mario in many games]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnUser Context triple: [Fire Flower, effectOnUser, changes outfit color to white and red for Mario in many games]
-
A.
effectOnOthers
Indicates the impact or influence that one entity’s actions, presence, or state has on other entities.
-
B.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
-
C.
eventEffect
Indicates the resulting change, outcome, or consequence that one event has on another state, entity, or event.
-
D.
effectOnViewers
Indicates the impact or influence that something has on those who observe or experience it.
-
E.
effectOnMembers
Indicates the impact or influence that something has on the members of a group or organization.
- 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_69c6888b5248819090499a884ee3ec39 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e8e2506881909fc4e81b9b79e873 |
completed | March 27, 2026, 8:30 p.m. |
| PD | Predicate disambiguation | batch_69c6e752385c819096fbab55566ee2a8 |
completed | March 27, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69c6e8b5f6508190af28e06a7959d717 |
completed | March 27, 2026, 8:29 p.m. |
Created at: March 27, 2026, 2:50 p.m.