Triple
T15308810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tell Tale |
E365973
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Beatrice Miller |
E1015362
|
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: Beatrice Miller | Statement: [Tell Tale, stars, Beatrice Miller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beatrice Miller Context triple: [Tell Tale, stars, Beatrice Miller]
-
A.
Beatrice Miller
chosen
Beatrice Miller is an American singer and actress who gained prominence as a contestant on the U.S. version of The X Factor.
-
B.
Beatrice Sisul
Beatrice Sisul is a film editor known for her work on the thriller "Message from the King."
-
C.
Beatrice Hennessy
Beatrice Hennessy is the child of Tamar Teresa Day Hennessy.
-
D.
Beatrice Carbone
Beatrice Carbone is a central character in Arthur Miller’s play "A View from the Bridge," portrayed as Eddie Carbone’s loyal yet conflicted wife whose emotional insight and moral clarity highlight the family’s growing tensions.
-
E.
Beatrice Silverman
Beatrice Silverman was the first wife of American novelist and journalist Norman Mailer, whom he married while a student at Harvard.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03cd176708190b0f6ba17aed92f8e |
completed | April 16, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef89feda88190b18f6a03d6e968aa |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:16 a.m.