Triple
T10771801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zabar's |
E254097
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object | Lillian Zabar |
E884894
|
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: Lillian Zabar | Statement: [Zabar's, foundedBy, Lillian Zabar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lillian Zabar Context triple: [Zabar's, foundedBy, Lillian Zabar]
-
A.
Louis Zabar
chosen
Louis Zabar was a New York City grocer and entrepreneur best known as the co-founder of the iconic Upper West Side gourmet food emporium Zabar’s.
-
B.
Myrtle Gruenert
Myrtle Gruenert was the wife of Australian speech therapist Lionel Logue, who famously treated King George VI.
-
C.
Sidney Taylor
Sidney Taylor is a banking executive who served in a supervisory leadership role at Meritor Savings Bank, FSB.
-
D.
Zina Dizengoff
Zina Dizengoff was the wife of Meir Dizengoff, the first mayor of Tel Aviv, and a notable early resident associated with the city's founding era.
-
E.
Hillel Hassenfeld
Hillel Hassenfeld was an American businessman best known as a co-founder of the toy and board game company that became Hasbro.
- 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_69d6aa5f54f4819082d0bbcb6f8797e6 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d7329a281081909cdc4b971cf69207 |
completed | April 9, 2026, 5:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de55cbbecc81908c2ddf2739ce7ffe |
completed | April 14, 2026, 2:57 p.m. |
Created at: April 8, 2026, 9:16 p.m.