Triple
T4396221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unger |
E99493
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Deborah Unger |
E364598
|
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: Deborah Unger | Statement: [Unger, hasNotableBearer, Deborah Unger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deborah Unger Context triple: [Unger, hasNotableBearer, Deborah Unger]
-
A.
Deborah Kara Unger
chosen
Deborah Kara Unger is a Canadian actress known for her intense, often edgy performances in films such as "Crash," "The Game," and "Silent Hill."
-
B.
Deborah Waxman
Deborah Waxman is an American rabbi and scholar who serves as a leading contemporary voice and institutional leader within Reconstructionist Judaism.
-
C.
Deborah Pines
Deborah Pines is an American physician and writer best known as the wife of journalist and author Tony Schwartz.
-
D.
Deborah Fallender
Deborah Fallender is an American actress best known for her work in film and television during the 1970s and 1980s.
-
E.
June Preisser
June Preisser was an American film actress and dancer best known for her energetic supporting roles in 1930s and 1940s Hollywood musicals, often playing peppy, acrobatic teenagers.
- 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_69b345506b408190b0e3dee616738a7d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352ab928c81909f4406d5df3e081b |
completed | March 12, 2026, 11:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becf8f926481908f6e1cf33fc79a04 |
completed | March 21, 2026, 5:04 p.m. |
Created at: March 12, 2026, 11:20 p.m.