Triple
T37464471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Twinspell |
E930999
|
entity |
| Predicate | copyHasTwinspellKeyword |
P29689
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [Twinspell, copyHasTwinspellKeyword, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: copyHasTwinspellKeyword Context triple: [Twinspell, copyHasTwinspellKeyword, false]
-
A.
sharesSpellingWith
chosen
Indicates that two entities have identical or substantially identical written forms (i.e., they are spelled the same way).
-
B.
hasTwinCharacters
Indicates that two characters are twins, sharing the same parents and birth time or very close birth times.
-
C.
hasVariantSpelling
Indicates that one term is an alternative spelling form of another term.
-
D.
isPlayOnWordsWith
Indicates a relationship where one expression is a pun or wordplay that depends on, echoes, or cleverly twists the wording or meaning of another expression.
-
E.
hasTwinWork
Indicates that one work has a corresponding counterpart considered its twin, typically due to strong similarity, parallel creation, or intentional pairing.
- 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_69fb92efc5948190a040ba2028bab964 |
completed | May 6, 2026, 7:13 p.m. |
| PD | Predicate disambiguation | batch_69fb8d0b52588190bb29937a43b99b5e |
completed | May 6, 2026, 6:48 p.m. |
Created at: May 3, 2026, 4:17 p.m.