Triple
T12967987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Haas |
E321316
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Betty Haas |
E321316
|
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: Betty Haas | Statement: [Betty Haas, name, Betty Haas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betty Haas Context triple: [Betty Haas, name, Betty Haas]
-
A.
Betty Haas
chosen
Betty Haas is known as the former wife of American politician and longtime U.S. Senator Joe Lieberman.
-
B.
Betty Reinhardt
Betty Reinhardt was a screenwriter best known for her work on the classic 1944 film noir "Laura."
-
C.
Mary Haas
Mary Haas was an influential American linguist renowned for her work on Native American languages and for training a generation of field linguists in the Boasian tradition.
-
D.
Betty Schneider
Betty Schneider is a French actress best known for her leading role in Jacques Rivette’s influential New Wave film "Paris Belongs to Us."
-
E.
Betty Kaplan
Betty Kaplan is a film director and screenwriter best known for adapting literary works, including Isabel Allende’s novel "Of Love and Shadows," for the screen.
- 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_69d80763bd6c819094437da5b20b01d2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e3f702481908f0f90f4f12d3f4d |
completed | April 10, 2026, 10:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1af38248190a85d0fa3a26c3d08 |
completed | May 6, 2026, 8:16 p.m. |
Created at: April 9, 2026, 8:32 p.m.