Triple
T17973176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexa Demie |
E449397
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Mid90s |
—
|
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: Mid90s | Statement: [Alexa Demie, notableWork, Mid90s]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mid90s Context triple: [Alexa Demie, notableWork, Mid90s]
-
A.
Mid90s
chosen
Mid90s is a coming-of-age film set in 1990s Los Angeles that follows a young boy who finds friendship and identity in a group of skateboarders.
-
B.
The 99
The 99 is a comic book series and media franchise featuring a team of superheroes inspired by the 99 attributes of Allah from Islamic tradition.
-
C.
The Get Down
The Get Down is a musical drama television series set in 1970s New York City that chronicles the rise of hip-hop and disco through the lives of South Bronx teenagers.
-
D.
MobbDeen
MobbDeen is a music producer known for contributing to A$AP Rocky’s album "At. Long. Last. A$AP."
-
E.
Overheard
Overheard is a Hong Kong crime thriller film featuring Daniel Wu in a story centered on high-stakes surveillance and financial intrigue.
- 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_69d8b9f9927c8190a006110c8b996e61 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4b1fca04481908f0dd875953fd82f |
completed | April 19, 2026, 10:44 a.m. |
Created at: April 10, 2026, 10:22 a.m.