Triple
T6615271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ordinary Love |
E149333
|
entity |
| Predicate | musicVideoDirector |
P4911
|
FINISHED |
| Object |
Mac Premo
Mac Premo is an American artist, filmmaker, and commercial director known for his inventive mixed-media work and visually distinctive short films and music videos.
|
E599802
|
NE FINISHED |
How this triple was built (4 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: Mac Premo | Statement: [Ordinary Love, musicVideoDirector, Mac Premo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mac Premo Context triple: [Ordinary Love, musicVideoDirector, Mac Premo]
-
A.
Cowher
Cowher is the surname of Bill Cowher, the former Pittsburgh Steelers head coach and Super Bowl–winning NFL analyst.
-
B.
Divac
Divac is a Serbian surname most prominently associated with former NBA center and basketball executive Vlade Divac.
-
C.
Billups
Billups is a surname most notably associated with former NBA player and current coach Chauncey Billups.
-
D.
Tony Meola
Tony Meola is a former American soccer goalkeeper best known for starring with the U.S. national team in the 1990 and 1994 World Cups and for his standout career in Major League Soccer.
-
E.
Marv Murchins
Marv Murchins is one of the bumbling burglar duo known as the "Wet Bandits" in the comedy film *Home Alone*.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mac Premo Triple: [Ordinary Love, musicVideoDirector, Mac Premo]
Generated description
Mac Premo is an American artist, filmmaker, and commercial director known for his inventive mixed-media work and visually distinctive short films and music videos.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mac Premo Target entity description: Mac Premo is an American artist, filmmaker, and commercial director known for his inventive mixed-media work and visually distinctive short films and music videos.
-
A.
Cowher
Cowher is the surname of Bill Cowher, the former Pittsburgh Steelers head coach and Super Bowl–winning NFL analyst.
-
B.
Divac
Divac is a Serbian surname most prominently associated with former NBA center and basketball executive Vlade Divac.
-
C.
Billups
Billups is a surname most notably associated with former NBA player and current coach Chauncey Billups.
-
D.
Tony Meola
Tony Meola is a former American soccer goalkeeper best known for starring with the U.S. national team in the 1990 and 1994 World Cups and for his standout career in Major League Soccer.
-
E.
Marv Murchins
Marv Murchins is one of the bumbling burglar duo known as the "Wet Bandits" in the comedy film *Home Alone*.
- F. None of above. chosen
Provenance (5 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_69c687ebc680819094caf71faba2efe2 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6af569ecc8190a9526decc745f0a0 |
completed | March 27, 2026, 4:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbda469481908173db345ba2f216 |
completed | March 27, 2026, 6:26 p.m. |
| NEDg | Description generation | batch_69c6cd428b988190b01311ca02f4dff3 |
completed | March 27, 2026, 6:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6cdcc10c08190aa98212bd17063a3 |
completed | March 27, 2026, 6:34 p.m. |
Created at: March 27, 2026, 1:57 p.m.