Triple
T5751962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken |
E126873
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Ruth Handler |
E509791
|
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 Handler | Statement: [Ken, creator, Ruth Handler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ruth Handler Context triple: [Ken, creator, Ruth Handler]
-
A.
Phyllis Lindstrom
Phyllis Lindstrom is a snobbish, self-absorbed yet comically endearing neighbor and friend in the classic American sitcom "The Mary Tyler Moore Show."
-
B.
Mary Kay Ash
chosen
Mary Kay Ash was an American businesswoman and entrepreneur best known as the founder of Mary Kay Cosmetics, a pioneering direct-sales cosmetics company.
-
C.
Shari Arison
Shari Arison is an Israeli-American businesswoman and philanthropist known as one of Israel’s wealthiest women and a major shareholder in Bank Hapoalim.
-
D.
Margo Winkler
Margo Winkler is an American actress known for her frequent small roles in films produced or directed by her husband, filmmaker Irwin Winkler.
-
E.
Betty Steinberg
Betty Steinberg is a film editor best known for her work on the crime drama film "The Killing."
- 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_69c00832aedc81909899801b141fa3b4 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0288b580c81909e1289982b106695 |
completed | March 22, 2026, 5:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07e3a50b88190a943b2d91d3c5b8e |
completed | March 22, 2026, 11:41 p.m. |
Created at: March 22, 2026, 3:48 p.m.