Triple
T2988523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Francisco Seals |
E80687
|
entity |
| Predicate | competition |
P563
|
FINISHED |
| Object | Hollywood Stars |
E232191
|
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: Hollywood Stars | Statement: [San Francisco Seals, competition, Hollywood Stars]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hollywood Stars Context triple: [San Francisco Seals, competition, Hollywood Stars]
-
A.
Hollywood Stars
chosen
Hollywood Stars was a popular minor league baseball team based in Los Angeles that played in the Pacific Coast League and became known for its celebrity ownership and entertainment-industry flair.
-
B.
Moviefone
Moviefone is an online service that provides movie showtimes, tickets, and related film information to consumers.
-
C.
Stars
Stars is a common team name used by various sports franchises, notably including the former American Basketball Association team the Utah Stars.
-
D.
Hollywood
Hollywood is a famous Los Angeles neighborhood internationally recognized as the historic center of the American film and entertainment industry.
-
E.
Hollywood
Hollywood is a residential neighborhood in the city of College Park, Maryland, known for its suburban character and proximity to the University of Maryland.
- 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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99c9cdd081908fa8094a3ac1f8d3 |
completed | March 8, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b108fe2568819083a8f97b37dc8340 |
completed | March 11, 2026, 6:17 a.m. |
Created at: March 8, 2026, 2:59 p.m.