Triple
T15692043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Page Hannah |
E380354
|
entity |
| Predicate | sibling |
P363
|
FINISHED |
| Object | Tanya Wexler |
—
|
NE NERFINISHED |
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: Tanya Wexler | Statement: [Page Hannah, sibling, Tanya Wexler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tanya Wexler Context triple: [Page Hannah, sibling, Tanya Wexler]
-
A.
Tanya Wexler
chosen
Tanya Wexler is an American film director best known for her period romantic comedy "Hysteria" and her work on character-driven independent films.
-
B.
Rachel Leibowitz
Rachel Leibowitz is a person notable enough to be specifically cited as a bearer of the surname Leibowitz.
-
C.
Beth Wexler
Beth Wexler is a character in the film "Volunteers," a 1985 comedy about Peace Corps volunteers in Thailand.
-
D.
Shira Wolosky Weiss
Shira Wolosky Weiss is a scholar and author known for her work at the intersection of Jewish thought, literature, and philosophy, including co-authoring the book *Defending Identity*.
-
E.
Deborah Waxman
Deborah Waxman is an American rabbi and scholar who serves as a leading contemporary voice and institutional leader within Reconstructionist Judaism.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d86d99e860819094b6957cde470f2c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f4f5a888190bd3681bcb9bbc02f |
completed | April 16, 2026, 2:54 a.m. |
Created at: April 10, 2026, 4:44 a.m.