Triple
T3237093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enron: The Smartest Guys in the Room |
E67879
|
entity |
| Predicate | portrays |
P264
|
FINISHED |
| Object | Ken Lay |
E123928
|
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: Ken Lay | Statement: [Enron: The Smartest Guys in the Room, portrays, Ken Lay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ken Lay Context triple: [Enron: The Smartest Guys in the Room, portrays, Ken Lay]
-
A.
Kenneth Lay
chosen
Kenneth Lay was the longtime chairman and CEO of Enron, widely known for his central role in the massive Enron corporate fraud and accounting scandal of the early 2000s.
-
B.
Mike Lynch
Mike Lynch is a collegiate athletics administrator best known for leading the athletic department at Babson College.
-
C.
Michael Dell
Michael Dell is an American entrepreneur and business magnate best known as the founder and longtime CEO of Dell Technologies, one of the world’s largest technology companies.
-
D.
Thomas Siebel
Thomas Siebel is an American technology entrepreneur best known as the founder of Siebel Systems and later the cloud computing company C3.ai.
-
E.
Jim Lewis
Jim Lewis is a writer best known for his extensive work on Muppet-related projects, contributing scripts and material for various films, shows, and attractions.
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaef29bf48190a9aa3a39f0138428 |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b277459d1081909766934ce6a56091 |
completed | March 12, 2026, 8:20 a.m. |
Created at: March 8, 2026, 3:08 p.m.