Triple
T31628175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 99 Red Balloons |
E807082
|
entity |
| Predicate | hasEnglishAdaptationBy |
P86128
|
FINISHED |
| Object | Kevin McAlea |
—
|
NE NERFINISHED |
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: Kevin McAlea | Statement: [99 Red Balloons, hasEnglishAdaptationBy, Kevin McAlea]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEnglishAdaptationBy Context triple: [99 Red Balloons, hasEnglishAdaptationBy, Kevin McAlea]
-
A.
adaptedInLanguage
Indicates that a work or content has been modified or translated so it can be presented or understood in a specified language.
-
B.
hasEnglishEdition
Indicates that one entity has a version or edition of itself that is produced or available in the English language.
-
C.
hasAdaptationsIn
Indicates that something possesses or exhibits adaptations within a particular context, environment, or domain.
-
D.
languageOfMostAdaptations
Indicates the language in which the greatest number of adaptations of a given work or entity have been produced.
-
E.
hasNotableAdaptationBy
chosen
Indicates that an original work has a significant adaptation created by the specified adapting entity (such as a person, group, or organization).
- 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_69f348d7883c8190b6c13ab92b7ef076 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fb563aec448190875410fb1a3ed624 |
completed | May 6, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69fb35b9ede881908aaae93a215525df |
completed | May 6, 2026, 12:36 p.m. |
Created at: April 30, 2026, 10:44 p.m.