Triple
T18742996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Come Tomorrow |
E458335
|
entity |
| Predicate | track |
P17929
|
FINISHED |
| Object | She |
—
|
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: She | Statement: [Come Tomorrow, track, She]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: She Context triple: [Come Tomorrow, track, She]
-
A.
She
chosen
"She" is a track by the American punk rock band Green Day from their breakthrough 1994 album *Dookie*.
-
B.
She
"She" is a 1935 fantasy adventure film directed by Irving Pichel, best known for its exotic lost-world setting and early use of striking production design and special effects.
-
C.
She
"She" is a romantic ballad popularized by Elvis Costello, best known for his 1999 cover used in the film Notting Hill.
-
D.
She
"She" is a song by Harry Styles from his album "Fine Line," known for its dreamy, psychedelic rock sound and introspective lyrics about identity and desire.
-
E.
Her
"Her" is a lesser-known work by American poet, painter, and City Lights Books co-founder Lawrence Ferlinghetti, reflecting his characteristic Beat-influenced, avant-garde literary style.
- 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_69d8d394dc308190b6725073f5db324c |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e57690a4d081908ab4c3890bb07e86 |
completed | April 20, 2026, 12:42 a.m. |
Created at: April 10, 2026, 11:51 a.m.