Triple
T5348943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Results |
E124123
|
entity |
| Predicate | hasProducer |
P30366
|
FINISHED |
| Object | Chris Lowe |
E219908
|
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: Chris Lowe | Statement: [Results, hasProducer, Chris Lowe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chris Lowe Context triple: [Results, hasProducer, Chris Lowe]
-
A.
Chris Lowe
chosen
Chris Lowe is a British musician best known as the keyboardist and one half of the synth-pop duo Pet Shop Boys.
-
B.
Scott Barlow
Scott Barlow is an Australian businessman best known for his long-term leadership role as chairman and major stakeholder of A-League football club Sydney FC.
-
C.
John Owen Lowe
John Owen Lowe is an American actor and writer, known for his work on the Netflix series "Unstable" and for being the son of actor Rob Lowe.
-
D.
Mike Lowrey
Mike Lowrey is a stylish, wisecracking Miami detective portrayed by Will Smith in the Bad Boys action-comedy film series.
-
E.
Grant Bardsley
Grant Bardsley is a British voice actor best known for voicing the protagonist Taran in Disney’s animated film "The Black Cauldron."
- 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_69bd464be27081908807b40b75c1bbae |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd860ea7088190ad7be14132927d17 |
completed | March 20, 2026, 5:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21d0d4d08190a33c86553d2012fa |
completed | March 21, 2026, 10:55 p.m. |
Created at: March 20, 2026, 2:01 p.m.