Triple
T2464886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roberta Joan Anderson |
E55222
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Roberta |
E28738
|
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: [Roberta Joan Anderson, givenName, Roberta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roberta Context triple: [Roberta Joan Anderson, givenName, Roberta]
-
A.
Roberta
chosen
Roberta is a feminine given name commonly used in various languages, derived from the masculine name Robert.
-
B.
Roberta
"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.
-
C.
Rita
Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
-
D.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
E.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
- 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_69ab49e3622c8190ad22afa2c4fbb807 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd1216f44819094c46ae7c2c1e394 |
completed | March 7, 2026, 7:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1799b530819095d828c9d4a9dc9c |
completed | March 9, 2026, 6:55 p.m. |
Created at: March 6, 2026, 9:44 p.m.