Triple
T7022704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeffrey Katzenberg |
E162865
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Laura Katzenberg |
E636738
|
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: Laura Katzenberg | Statement: [Jeffrey Katzenberg, hasChild, Laura Katzenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Katzenberg Context triple: [Jeffrey Katzenberg, hasChild, Laura Katzenberg]
-
A.
Marilyn Katzenberg
chosen
Marilyn Katzenberg is an American philanthropist and political donor known for her charitable work and for being married to film executive Jeffrey Katzenberg.
-
B.
Beth Klarman
Beth Klarman is a member of the prominent Klarman family, known for its significant business success and philanthropic activities.
-
C.
Betsy Gotbaum
Betsy Gotbaum is an American public official and politician who served as New York City's Public Advocate in the early 2000s, acting as a citywide watchdog and ombudsman.
-
D.
Laurie Cahn
Laurie Cahn is a child of the famed American songwriter and lyricist Sammy Cahn.
-
E.
Laurie Tisch
Laurie Tisch is an American philanthropist and arts patron known for her significant contributions to education, culture, and community initiatives in New York City.
- 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_69c6885b26248190a857541e3d10e299 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e1edf3608190b7ba0bfb85710e97 |
completed | March 27, 2026, 8 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c78857d23c8190904a90459a802cb8 |
completed | March 28, 2026, 7:50 a.m. |
Created at: March 27, 2026, 2:35 p.m.