Triple
T8274450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Rogers (broadcaster) |
E193506
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mike |
E21023
|
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: Mike | Statement: [Mike Rogers (broadcaster), givenName, Mike]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Context triple: [Mike Rogers (broadcaster), givenName, Mike]
-
A.
Michael
chosen
Michael is a common masculine given name of Hebrew origin meaning "Who is like God?"
-
B.
Michael
"Michael" is a 1996 fantasy-comedy film starring John Travolta as an unconventional archangel visiting Earth.
-
C.
Mitch
Mitch is a masculine given name, often used as a short form of Mitchell.
-
D.
Micheal
Micheal is a given name, typically a variant spelling of the more common name Michael.
-
E.
Kevin
Kevin is the given name of Kevin Garnett, a Hall of Fame American professional basketball player known for his intensity, versatility, and NBA championship with the Boston Celtics.
- 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_69ca82e14ae481908ffdb822cd2192bc |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb798bc29c8190927ef4b0662d6b98 |
completed | March 31, 2026, 7:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cde75a2e048190b0de47a21e662baa |
completed | April 2, 2026, 3:49 a.m. |
Created at: March 30, 2026, 5:51 p.m.