Triple
T13246005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gordon Heath |
E315404
|
entity |
| Predicate | performedIn |
P795
|
FINISHED |
| Object | Anna Lucasta |
E1028315
|
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: Anna Lucasta | Statement: [Gordon Heath, performedIn, Anna Lucasta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Lucasta Context triple: [Gordon Heath, performedIn, Anna Lucasta]
-
A.
Anna Lucasta
chosen
Anna Lucasta is a mid-20th-century stage play, later adapted into films, that follows the turbulent life of a young woman estranged from her family and entangled in love, desire, and social prejudice.
-
B.
Maria Magdalena Keverich
Maria Magdalena Keverich was a German woman best known as the mother of the composer Ludwig van Beethoven.
-
C.
Luciana
Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
-
D.
Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
-
E.
Maria Aurora
Maria Aurora is a landlocked municipality in the province of Aurora in the Philippines, known for its rural landscapes and agricultural economy.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d5c09f88190bb1566a6d8c073a6 |
completed | April 10, 2026, 11:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a38d09881909e8e3c32e9b1746e |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:23 p.m.