Triple
T6147351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abraham Palatnik |
E137111
|
entity |
| Predicate | mother |
P120
|
FINISHED |
| Object |
Miriam Palatnik
Miriam Palatnik is the mother of Brazilian kinetic and optical art pioneer Abraham Palatnik.
|
E575986
|
NE FINISHED |
How this triple was built (4 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: Miriam Palatnik | Statement: [Abraham Palatnik, mother, Miriam Palatnik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miriam Palatnik Context triple: [Abraham Palatnik, mother, Miriam Palatnik]
-
A.
Miriam Weinstein
Miriam Weinstein is the mother of film producer Harvey Weinstein, whose first name inspired the name of the film company Miramax.
-
B.
Basya Cohen
Basya Cohen, better known as Betty Comden, was an American lyricist, screenwriter, and performer famed for her influential work on classic Broadway musicals and Hollywood films.
-
C.
Miriam Mendelsohn
Miriam Mendelsohn is a loyal, upbeat, and supportive best friend of Mei Lee in Pixar's animated film "Turning Red."
-
D.
Yona Wallach
Yona Wallach was an influential Israeli poet known for her experimental, provocative, and psychologically charged Hebrew poetry that challenged social and sexual norms.
-
E.
Miriam Bienstock
Miriam Bienstock was an American music industry executive and co-founder of Atlantic Records who played a key role in shaping the label’s early business operations and success.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Miriam Palatnik Triple: [Abraham Palatnik, mother, Miriam Palatnik]
Generated description
Miriam Palatnik is the mother of Brazilian kinetic and optical art pioneer Abraham Palatnik.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Miriam Palatnik Target entity description: Miriam Palatnik is the mother of Brazilian kinetic and optical art pioneer Abraham Palatnik.
-
A.
Miriam Weinstein
Miriam Weinstein is the mother of film producer Harvey Weinstein, whose first name inspired the name of the film company Miramax.
-
B.
Basya Cohen
Basya Cohen, better known as Betty Comden, was an American lyricist, screenwriter, and performer famed for her influential work on classic Broadway musicals and Hollywood films.
-
C.
Miriam Mendelsohn
Miriam Mendelsohn is a loyal, upbeat, and supportive best friend of Mei Lee in Pixar's animated film "Turning Red."
-
D.
Yona Wallach
Yona Wallach was an influential Israeli poet known for her experimental, provocative, and psychologically charged Hebrew poetry that challenged social and sexual norms.
-
E.
Miriam Bienstock
Miriam Bienstock was an American music industry executive and co-founder of Atlantic Records who played a key role in shaping the label’s early business operations and success.
- F. None of above. chosen
Provenance (5 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_69c008a2c6308190a56519b22d55d083 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05cdeeaa88190948d9db6eb2dbf46 |
completed | March 22, 2026, 9:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16ece259c8190b7258859a35ca4fd |
completed | March 23, 2026, 4:48 p.m. |
| NEDg | Description generation | batch_69c1c4a8f69c819086a0bd355e6750db |
completed | March 23, 2026, 10:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1c59ef9b0819091b05cd30c07d918 |
completed | March 23, 2026, 10:58 p.m. |
Created at: March 22, 2026, 4:16 p.m.