Triple
T16445079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nothing in Common |
E399402
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Max Basner |
E638661
|
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: Max Basner | Statement: [Nothing in Common, mainCharacter, Max Basner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Max Basner Context triple: [Nothing in Common, mainCharacter, Max Basner]
-
A.
Michael Breyer
Michael Breyer is the son of former U.S. Supreme Court Justice Stephen G. Breyer.
-
B.
Paul Knabenshue
Paul Knabenshue was an American diplomat best known for serving as the first U.S. Ambassador to Iraq in the early 20th century.
-
C.
Michael Begler
Michael Begler is an American television writer and producer best known for co-creating the period medical drama series "The Knick."
-
D.
Christian Specht
Christian Specht is a German politician who serves as the mayor of the city of Mannheim.
-
E.
Marc Blucas
chosen
Marc Blucas is an American actor best known for his roles in television series like "Buffy the Vampire Slayer" and various film and TV projects, often portraying athletic or military characters.
- 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_69d87f2c6778819080fcfae53be8f12a |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32cdb5d908190bb6c5cb3c794cf4b |
completed | April 18, 2026, 7:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f4b738881908f8a205466397f33 |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 5:10 a.m.