Triple
T20457004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Briggs |
E501816
|
entity |
| Predicate | associatedAct |
P37
|
FINISHED |
| Object | Grin |
—
|
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: Grin | Statement: [David Briggs, associatedAct, Grin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grin Context triple: [David Briggs, associatedAct, Grin]
-
A.
Grin
chosen
Grin was an early 1970s American rock band led by guitarist and singer-songwriter Nils Lofgren, known for its melodic, roots-influenced rock sound.
-
B.
Smiley Smile
Smiley Smile is a 1967 studio album by the Beach Boys, noted for its minimalist, lo-fi reworking of material from the abandoned Smile project.
-
C.
Smiles
Smiles is a 1930 musical comedy film featuring songs by composer Vincent Youmans.
-
D.
Smiles
Smiles is a Brazilian frequent-flyer and loyalty program that allows members to earn and redeem miles across flights, partner airlines, and various retail and service partners.
-
E.
Smiley Face
Smiley Face is a 2007 stoner comedy film starring Anna Faris as an out-of-work actress who embarks on a chaotic, marijuana-fueled day in Los Angeles.
- 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_69e0b4ad4940819098cf2ff6413574e5 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e696a1b03c8190984d9db6d3251308 |
completed | April 20, 2026, 9:12 p.m. |
Created at: April 16, 2026, 11:32 a.m.