Triple
T37464222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flobbidinous Floop |
E930993
|
entity |
| Predicate | copyEffectScope |
P128034
|
FINISHED |
| Object | last friendly minion you played |
—
|
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: last friendly minion you played | Statement: [Flobbidinous Floop, copyEffectScope, last friendly minion you played]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: copyEffectScope Context triple: [Flobbidinous Floop, copyEffectScope, last friendly minion you played]
-
A.
copyEffect
Indicates that one entity duplicates or reproduces the effect, behavior, or outcome originally produced by another entity.
-
B.
componentScope
Indicates that one entity defines or constrains the scope, visibility, or applicability of another component within a system or context.
-
C.
capturesEffectOf
Indicates that one entity represents or records the impact, consequence, or outcome produced by another entity or process.
-
D.
copySemantics
chosen
Indicates how the behavior or state of one entity is duplicated, shared, or independently copied in relation to another entity.
-
E.
encodingScope
Indicates the range or extent of content or information that is covered, represented, or captured by a particular encoding.
- 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_69f76ec1a1148190b0a961f188d621b0 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba68077788190b311e027435fcf87 |
completed | May 6, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fba34c65ac8190b298f0f00d1dcc0e |
completed | May 6, 2026, 8:23 p.m. |
Created at: May 3, 2026, 4:17 p.m.