Triple
T16785362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Otto Harbach |
E407956
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Roberta |
E266632
|
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: Roberta | Statement: [Otto Harbach, notableWork, Roberta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roberta Context triple: [Otto Harbach, notableWork, Roberta]
-
A.
Roberta
chosen
"Roberta" is a 1935 Hollywood musical film starring Fred Astaire (Frederick Austerlitz) and Ginger Rogers, known for its fashion-world setting and classic Jerome Kern songs.
-
B.
Roberta
Roberta is a feminine given name commonly used in various languages, derived from the masculine name Robert.
-
C.
Joanne
Joanne is a feminine given name of Hebrew origin, commonly used in English-speaking countries.
-
D.
Rosanna
Rosanna is a feminine given name of Latin origin, derived from a combination of "Rose" and "Anna."
-
E.
Rosanna
Rosanna is a residential suburb in Melbourne, Australia, known for its leafy streets, family-friendly atmosphere, and proximity to parklands and public transport.
- 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_69d8839270588190886720d9519bbf8f |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b21996cc81909deb88545af7079f |
completed | April 18, 2026, 4:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00ab07bdcc819086479178e5b8b679 |
completed | May 10, 2026, 3:57 p.m. |
Created at: April 10, 2026, 5:22 a.m.