Triple
T16880915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1-800-273-8255 |
E421411
|
entity |
| Predicate | musicVideoDirector |
P4911
|
FINISHED |
| Object | Andy Hines |
—
|
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: Andy Hines | Statement: [1-800-273-8255, musicVideoDirector, Andy Hines]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andy Hines Context triple: [1-800-273-8255, musicVideoDirector, Andy Hines]
-
A.
Jim Hines
Jim Hines was an American sprinter and Olympic gold medalist best known for being the first man to officially break the 10-second barrier in the 100-meter dash.
-
B.
Curtis Hixon
Curtis Hixon was a prominent Tampa, Florida mayor and civic leader whose contributions to the city led to major public landmarks being named in his honor.
-
C.
Andrew Hines
chosen
Andrew Hines is a music video director known for his creative visual work in the contemporary music industry.
-
D.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
-
E.
Matt Hulett
Matt Hulett is an American technology and business executive known for leading and scaling multiple software and digital media companies.
- 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_69d889d470fc8190b4aec199636c0c56 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3b7fc61a08190b9f611c06a95be01 |
completed | April 18, 2026, 4:57 p.m. |
Created at: April 10, 2026, 5:29 a.m.