Triple
T5252934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cyndi Lauper |
E118630
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Cynthia |
E48557
|
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: Cynthia | Statement: [Cyndi Lauper, givenName, Cynthia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cynthia Context triple: [Cyndi Lauper, givenName, Cynthia]
-
A.
Cynthia
chosen
Cynthia is a common feminine given name used in various cultures, often associated with the Greek moon goddess Artemis.
-
B.
Cindy
Cindy is a fictional character from the short-lived 1970s American sitcom "Blansky's Beauties," which followed the lives of Las Vegas showgirls.
-
C.
Cindy
Cindy is a fictional character from the action film "Commando," appearing as part of the movie’s high-stakes rescue storyline.
-
D.
Tricia
Tricia is a feminine given name commonly used as a shortened or informal form of Patricia.
-
E.
Adrienne
Adrienne is a feminine given name of French origin, commonly used in English- and French-speaking countries.
- 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_69bd446978108190bb5f9c5c23d93f88 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7b7cd7f4819098e591df07564a52 |
completed | March 20, 2026, 4:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69befe72c91c8190ab0f988dca420399 |
completed | March 21, 2026, 8:24 p.m. |
Created at: March 20, 2026, 1:50 p.m.