Triple
T15011096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shark Tank |
E377836
|
entity |
| Predicate | hasRecurringGuestInvestor |
P90709
|
FINISHED |
| Object | Sara Blakely |
E404061
|
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: Sara Blakely | Statement: [Shark Tank, hasRecurringGuestInvestor, Sara Blakely]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sara Blakely Context triple: [Shark Tank, hasRecurringGuestInvestor, Sara Blakely]
-
A.
Sara Blakely
chosen
Sara Blakely is an American entrepreneur and billionaire best known as the founder of the shapewear company Spanx.
-
B.
Paige Mycoskie
Paige Mycoskie is an American entrepreneur and designer best known as the founder of the vintage-inspired clothing and lifestyle brand Aviator Nation.
-
C.
Sheryl Yoast
Sheryl Yoast is a character in the film "Remember the Titans," depicted as the passionate, football-loving daughter of coach Bill Yoast.
-
D.
Mary Kay Ash
Mary Kay Ash was an American businesswoman and entrepreneur best known as the founder of Mary Kay Cosmetics, a pioneering direct-sales cosmetics company.
-
E.
Sandy Lerner
Sandy Lerner is an American businesswoman and philanthropist best known as the co-founder of networking giant Cisco Systems and later as a supporter of animal welfare, sustainable agriculture, and literary scholarship.
- 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_69d85cd3a3c881908c71fc424d459c17 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded734943481908dad4ceed4fe850c |
completed | April 15, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe96a7dcac8190b0153d7cdac03afa |
completed | May 9, 2026, 2:06 a.m. |
Created at: April 10, 2026, 2:55 a.m.