Triple
T10280857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Mudd |
E241095
|
entity |
| Predicate | produced |
P490
|
FINISHED |
| Object | The Good Lie |
E514205
|
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: The Good Lie | Statement: [Mr. Mudd, produced, The Good Lie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Good Lie Context triple: [Mr. Mudd, produced, The Good Lie]
-
A.
The Good Lie
chosen
The Good Lie is a 2014 drama film about Sudanese refugees resettling in the United States, starring Reese Witherspoon and directed by Philippe Falardeau.
-
B.
The Great Lie
The Great Lie is a 1941 American drama film starring Bette Davis and Mary Astor, noted for Astor’s Oscar-winning supporting performance.
-
C.
The Lying
"The Lying" is a musical work by American singer-songwriter Henry Wolfe, known for its introspective lyrics and indie-folk sensibility.
-
D.
City of Lies
City of Lies is a crime novel by Alafair Burke featuring a savvy protagonist entangled in deception and danger within New York’s legal and media worlds.
-
E.
Separate Lies
Separate Lies is a 2005 British drama film, adapted from Nigel Balchin’s novel "A Way Through the Wood," about a seemingly perfect upper-class marriage unraveling after a hit-and-run accident.
- 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2a0c90c8190ad6ee479a32e5a95 |
completed | April 7, 2026, 9:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f82f16688190b1c6b80e424bd552 |
completed | April 9, 2026, 12:51 a.m. |
Created at: April 6, 2026, 11:38 a.m.