Triple
T3624851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marla Lerner Tanenbaum |
E76812
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lerner |
E181685
|
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: Lerner | Statement: [Marla Lerner Tanenbaum, familyName, Lerner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lerner Context triple: [Marla Lerner Tanenbaum, familyName, Lerner]
-
A.
Lerner
chosen
Lerner is a surname most notably associated with Sandy Lerner, the co-founder of Cisco Systems and a prominent philanthropist and businesswoman.
-
B.
Ted Lerner
Ted Lerner was an American real estate developer and principal owner of the Washington Nationals Major League Baseball team.
-
C.
Lester
Lester is the given name of Lester B. Pearson, the Canadian diplomat, Nobel Peace Prize laureate, and 14th prime minister of Canada.
-
D.
Lester
Lester is a surname of Irish origin borne by various notable individuals, including the diplomat Seán Lester.
-
E.
Lester
Lester is the central character in the 2016 puzzle-platform video game "Mekazoo" (also known as "Makers" in some regions), around whom the game's story and gameplay revolve.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2d9845c8190ad65b2471000dfa0 |
completed | March 8, 2026, 6:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b44f0e8d1c8190ae1728d07d5a9e87 |
completed | March 13, 2026, 5:53 p.m. |
Created at: March 8, 2026, 3:23 p.m.