Triple
T5104262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cats (musical) |
E115051
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Macavity |
E119208
|
NE 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: Macavity | Statement: [Cats (musical), hasCharacter, Macavity]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Macavity Context triple: [Cats (musical), hasCharacter, Macavity]
-
A.
Macavity
chosen
Macavity is a mysterious master criminal cat from T. S. Eliot’s poetry, famously dubbed “the Napoleon of Crime.”
-
B.
Mr. Mistoffelees
Mr. Mistoffelees is a magical black-and-white cat character from T. S. Eliot’s poetry, best known today through his prominent role in the musical "Cats."
-
C.
The Rum Tum Tugger
The Rum Tum Tugger is a flamboyant, attention-seeking cat character from T. S. Eliot’s poetry, later popularized in Andrew Lloyd Webber’s musical "Cats."
-
D.
Henrietta Pussycat
Henrietta Pussycat is a shy, sweet, and polite puppet cat who lives in the Neighborhood of Make-Believe on the children's television series "Mister Rogers' Neighborhood."
-
E.
Mr. Whiskers
Mr. Whiskers is the talking, psychopathic cat companion voiced by Ryan Reynolds in the dark comedy film "The Voices."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd4440b3348190be1251fd8b7951f1 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7588c1cc81909d380f91ee214808 |
completed | March 20, 2026, 4:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba95dbd48190a7d87f3af77424e0 |
completed | March 21, 2026, 3:34 p.m. |
Created at: March 20, 2026, 1:41 p.m.