Triple
T10339036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sally Kellerman |
E243080
|
entity |
| Predicate | televisionAppearance |
P795
|
FINISHED |
| Object | Maron |
E604795
|
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: Maron | Statement: [Sally Kellerman, televisionAppearance, Maron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maron Context triple: [Sally Kellerman, televisionAppearance, Maron]
-
A.
Maron
Maron is a semi-autobiographical comedy television series created by and starring comedian Marc Maron, loosely based on his life and career.
-
B.
Marino
Marino is a surname most famously associated with Dan Marino, the Hall of Fame former NFL quarterback for the Miami Dolphins.
-
C.
Marino
Marino is a historic town in Italy’s Alban Hills near Rome, known for its wine production and annual grape festival.
-
D.
Maron (TV series)
chosen
Maron is a semi-autobiographical comedy series created by and starring comedian Marc Maron, loosely based on his life and popular WTF podcast.
-
E.
Mateur
Mateur is a town in northern Tunisia known as an agricultural and transport hub situated near Lake Ichkeul.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e0a470948190959f298dd6110bf3 |
completed | April 7, 2026, 10:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7506278f881908b090b13706e5d4e |
completed | April 9, 2026, 7:08 a.m. |
Created at: April 6, 2026, 11:54 a.m.