Triple
T23494201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Feehily |
E571656
|
entity |
| Predicate | hasFirstName |
P17
|
FINISHED |
| Object | Markus |
—
|
NE NERFINISHED |
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: Markus | Statement: [Mark Feehily, hasFirstName, Markus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Markus Context triple: [Mark Feehily, hasFirstName, Markus]
-
A.
Markus
chosen
Markus is a masculine given name of Latin origin, commonly used in various European countries and derived from the name Marcus.
-
B.
Markus
Markus is the given first name of the renowned abstract expressionist painter Mark Rothko.
-
C.
Markus
Markus is the first name of American professional baseball star Mookie Betts.
-
D.
John Markus
John Markus is an American television writer and producer best known for his work on sitcoms such as The Cosby Show and for creating the series Kristin.
-
E.
Markus Mobius
Markus Möbius is an economist known for his research in behavioral and experimental economics, often collaborating on influential empirical studies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b4829881909b77a70e942bbd54 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a7de1ab88190b6c2441c63a99713 |
completed | April 29, 2026, 6:40 a.m. |
Created at: April 17, 2026, 6:05 p.m.