Triple
T15576686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Top Girls |
E374386
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Patient Griselda |
E603490
|
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: Patient Griselda | Statement: [Top Girls, character, Patient Griselda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Patient Griselda Context triple: [Top Girls, character, Patient Griselda]
-
A.
Griselda
chosen
Griselda is an opera by Alessandro Scarlatti, based on the medieval tale of patient Griselda and notable for its Baroque musical style and dramatic intensity.
-
B.
Rosita
Rosita is a companion character who appears alongside the Doctor in the "Doctor Who" special episode "The Next Doctor."
-
C.
Rosita
Rosita is a bilingual, turquoise monster Muppet on Sesame Street known for introducing Spanish language and Latino culture to the show.
-
D.
Rosita
Rosita is a shy but talented pig and devoted mother who becomes a standout performer in the animated musical film "Sing."
-
E.
Griselda Siciliani
Griselda Siciliani is an Argentine actress and singer known for her work in television, film, and musical theatre.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e22c89081909b1ec0cd36a1ef45 |
completed | April 16, 2026, 2:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c4978ec8190a57de5d9a2ec6653 |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 4:10 a.m.