Triple
T28902800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mia Sara as Princess Lili |
E732993
|
entity |
| Predicate | alternateScoreComposer |
P169971
|
FINISHED |
| Object | Tangerine Dream |
—
|
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: Tangerine Dream | Statement: [Mia Sara as Princess Lili, alternateScoreComposer, Tangerine Dream]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alternateScoreComposer Context triple: [Mia Sara as Princess Lili, alternateScoreComposer, Tangerine Dream]
-
A.
alternativeScoring
Indicates that there exists an additional or non-standard method of scoring or evaluation associated with the primary scoring system or context.
-
B.
winnerComposer
Indicates that the subject is the composer who won a particular contest, award, or competitive event.
-
C.
alsoScoredFor
Indicates that an individual who scored for one team or entity has also scored for another team or entity.
-
D.
dedicatedComposerOfVariations
Indicates that one entity is the composer specifically responsible for creating a set of musical variations associated with another entity.
-
E.
usesCompositeScore
Indicates that an entity bases its evaluation, decision, or outcome on a combined score derived from multiple underlying metrics or factors.
- F. None of above. chosen
Provenance (4 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_69f05b08c2008190ac426a035a2ed66d |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f688d015908190ad5df37030ecf332 |
completed | May 2, 2026, 11:29 p.m. |
| PD | Predicate disambiguation | batch_69f68609c0b08190a8e1238a4d97c270 |
completed | May 2, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69f688034580819086a0f9100645f8ba |
completed | May 2, 2026, 11:25 p.m. |
Created at: April 28, 2026, 8:04 a.m.