Triple
T14643282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harbaugh Bowl |
E343779
|
entity |
| Predicate | referee |
P268
|
FINISHED |
| Object | Jerome Boger |
E444099
|
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: Jerome Boger | Statement: [Harbaugh Bowl, referee, Jerome Boger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jerome Boger Context triple: [Harbaugh Bowl, referee, Jerome Boger]
-
A.
Jerome Boger
chosen
Jerome Boger is a former NFL official best known for serving as the head referee in Super Bowl XLVII.
-
B.
Charles Begole
Charles Begole was an American mountaineer best known as one of the first climbers to reach the summit of Mount Whitney in the 19th century.
-
C.
Charles Guggenheim
Charles Guggenheim was an American documentary filmmaker renowned for his politically engaged and historically focused films, earning multiple Academy Awards over his career.
-
D.
Glenn Berger
Glenn Berger is an American screenwriter best known for co-writing major animated films such as the Kung Fu Panda series.
-
E.
Fred Schuler
Fred Schuler is a cinematographer best known for his work on films such as the 1980 comedy "Stir Crazy."
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4e80aa48190884bab800f357106 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff218b93d48190a7e16c3934828aa8 |
completed | May 9, 2026, 11:59 a.m. |
Created at: April 10, 2026, 1:26 a.m.