Triple
T8500940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugh Hefner |
E201211
|
entity |
| Predicate | parent |
P120
|
FINISHED |
| Object | Grace Hefner |
E740058
|
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: Grace Hefner | Statement: [Hugh Hefner, parent, Grace Hefner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grace Hefner Context triple: [Hugh Hefner, parent, Grace Hefner]
-
A.
Helen Gurley Brown
Helen Gurley Brown was an influential American author, editor, and longtime editor-in-chief of Cosmopolitan magazine, known for her pioneering work in the sexual revolution and modern feminism.
-
B.
Christie Hefner
chosen
Christie Hefner is an American businesswoman best known for serving as longtime CEO and chairwoman of Playboy Enterprises.
-
C.
Tina Brown
Tina Brown is an American magazine editor, journalist, and author best known for revitalizing publications such as Vanity Fair and The New Yorker.
-
D.
Hugh Hefner
Hugh Hefner was an American magazine publisher and cultural figure best known as the founder of Playboy and its associated lifestyle brand.
-
E.
Diana Vreeland
Diana Vreeland was a legendary fashion editor and style icon who shaped 20th-century fashion through her influential work at major magazines and as a consultant to the Metropolitan Museum of Art’s Costume Institute.
- 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_69ca831fe47c8190b5c57b456d2aefa0 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe5996ce88190956cb3f8d9ad3daf |
completed | March 31, 2026, 3:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6d1c2ce4819082c92484d3865edf |
completed | April 2, 2026, 1:20 p.m. |
Created at: March 30, 2026, 6:14 p.m.