Triple
T5104259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cats (musical) |
E115051
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Rum Tum Tugger |
E115053
|
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: Rum Tum Tugger | Statement: [Cats (musical), hasCharacter, Rum Tum Tugger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rum Tum Tugger Context triple: [Cats (musical), hasCharacter, Rum Tum Tugger]
-
A.
The Rum Tum Tugger
chosen
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."
-
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.
Aristocat the Tiger
Aristocat the Tiger is the costumed tiger mascot that represents Tennessee State University at its athletic events and campus activities.
-
D.
Mr. Goodkat
Mr. Goodkat is a mysterious, highly skilled hitman central to the plot of the crime thriller film "Lucky Number Slevin."
-
E.
Macavity
Macavity is a mysterious master criminal cat from T. S. Eliot’s poetry, famously dubbed “the Napoleon of Crime.”
- 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_69becfc94eb08190bd6ed138acca3171 |
completed | March 21, 2026, 5:05 p.m. |
Created at: March 20, 2026, 1:41 p.m.