Triple
T37464486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Twinspell |
E930999
|
entity |
| Predicate | keywordTextExample |
P168052
|
FINISHED |
| Object | Twinspell: Cast this, then add a copy to your hand |
—
|
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: Twinspell: Cast this, then add a copy to your hand | Statement: [Twinspell, keywordTextExample, Twinspell: Cast this, then add a copy to your hand]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: keywordTextExample Context triple: [Twinspell, keywordTextExample, Twinspell: Cast this, then add a copy to your hand]
-
A.
keyText
Indicates that a piece of text functions as a key or identifier used to access, reference, or unlock something in a system or context.
-
B.
keyExample
Indicates that something serves as a representative or illustrative instance of a key concept, feature, or case.
-
C.
keyTextualTheme
Indicates that one entity expresses or represents the primary textual theme or central idea associated with another entity.
-
D.
hasExampleKeyword
chosen
Indicates that something is associated with or contains a specific example keyword.
-
E.
keyTerm
Indicates that a term functions as a primary or central concept within a given context or information structure.
- 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_69fbb084760c8190a1554985d3c3cb7a |
completed | May 6, 2026, 9:20 p.m. |
| PD | Predicate disambiguation | batch_69fbadf3cb548190ba3b7514f76b790a |
completed | May 6, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:17 p.m.