Triple
T13912732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Thiang |
E334538
|
entity |
| Predicate | portrayedOnStageBy |
P9616
|
FINISHED |
| Object | Ruth Kobart |
E340200
|
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: Ruth Kobart | Statement: [Lady Thiang, portrayedOnStageBy, Ruth Kobart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ruth Kobart Context triple: [Lady Thiang, portrayedOnStageBy, Ruth Kobart]
-
A.
Ruth Kobart
chosen
Ruth Kobart was an American character actress known for her work on stage, film, and television, including roles in Broadway productions and various TV series.
-
B.
Ruth Wenger
Ruth Wenger was a Swiss singer and writer best known for her brief marriage to Nobel Prize–winning author Hermann Hesse.
-
C.
Ruth Weinstein
Ruth Weinstein is one of the children of disgraced American film producer Harvey Weinstein.
-
D.
Ruth Arnon
Ruth Arnon is an Israeli biochemist best known as a co-developer of the multiple sclerosis drug Copaxone and a prominent figure in immunology research.
-
E.
Margaret Shenberg
Margaret Shenberg was the first wife of influential Hollywood film producer and studio executive Louis B. Mayer.
- 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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de27245c648190b2946845ce0fdbf8 |
completed | April 14, 2026, 11:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6d72a17c8190b63f9f441731917d |
completed | May 8, 2026, 4:58 a.m. |
Created at: April 9, 2026, 10:16 p.m.