Triple
T9670413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosie Shuster |
E234010
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rosie |
E487544
|
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: Rosie | Statement: [Rosie Shuster, givenName, Rosie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosie Context triple: [Rosie Shuster, givenName, Rosie]
-
A.
Rosie
chosen
Rosie is a common diminutive given name typically used as a familiar or affectionate form of Rosemary and similar names.
-
B.
Rosie
Rosie is the nickname for the Rose M. Singer Center, a women’s jail facility on New York City’s Rikers Island.
-
C.
Rosie
"Rosie" is a novel by British gardener, broadcaster, and author Alan Titchmarsh, showcasing his talent for warm, character-driven storytelling beyond his well-known horticultural work.
-
D.
Rosie
Rosie is a black widow spider and circus performer in Pixar's animated film "A Bug's Life," known for her tough yet nurturing personality.
-
E.
Rosie
Rosie is a fictional character from the short-lived 1970s American sitcom "Blansky's Beauties," which followed the lives of Las Vegas showgirls and their manager.
- 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_69ca848f55e48190b3f67252571c3d45 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9c3ec17081908c2da74a1d1f49da |
completed | April 1, 2026, 10:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18a247ca48190910624dfbf0b491d |
completed | April 4, 2026, 10:01 p.m. |
Created at: March 30, 2026, 8:15 p.m.