Triple
T15368024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Armchair Expert |
E367466
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Monica Padman |
E1153124
|
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: Monica Padman | Statement: [Armchair Expert, producer, Monica Padman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monica Padman Context triple: [Armchair Expert, producer, Monica Padman]
-
A.
Monica Padman
chosen
Monica Padman is an American podcast producer, actor, and writer best known as Dax Shepard’s co-host and creative partner on the popular podcast "Armchair Expert."
-
B.
Kajal Gupta
Kajal Gupta is an actress known for her work in Tollywood, the Bengali-language film industry based in Kolkata.
-
C.
Kavita Rao
Kavita Rao is a fictional geneticist in the X-Men universe known for developing a controversial "cure" for mutant powers.
-
D.
Bhumika Chawla
Bhumika Chawla is an Indian actress known for her work in Hindi, Telugu, and Tamil films, including notable roles in movies like "Tere Naam" and "Gandhi, My Father."
-
E.
Devi Parikh
Devi Parikh is a computer vision and AI researcher known for her work on visual question answering, human-AI collaboration, and interpretable machine learning.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e4a7cdc8190b7b48c97e774c306 |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c30ae4c8190b7a4739983963e86 |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 3:18 a.m.