Triple
T19872694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woman to Woman |
E477556
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | Wonderland |
—
|
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: Wonderland | Statement: [Woman to Woman, hasTrack, Wonderland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wonderland Context triple: [Woman to Woman, hasTrack, Wonderland]
-
A.
Wonderland
Wonderland is a rapid transit station in Revere, Massachusetts, serving as the northern terminus of Boston’s MBTA Blue Line.
-
B.
Wonderland
chosen
Wonderland is a song by the British pop group Take That, known for its upbeat, anthemic style and inclusion on their 2017 album of the same name.
-
C.
Wonderland
Wonderland is a 1999 British drama film directed by Michael Winterbottom that interweaves the lives of several Londoners over a Guy Fawkes Night weekend.
-
D.
Wonderland
Wonderland is a 1971 novel by Joyce Carol Oates that follows the psychologically intense and often disturbing life journey of a brilliant but traumatized man in American society.
-
E.
Wonderland
"Wonderland" is a dramatic work by Eric Bogosian that explores dark, contemporary themes through his signature intense, character-driven storytelling.
- 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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658d826f88190be04188997952d1b |
completed | April 20, 2026, 4:48 p.m. |
Created at: April 10, 2026, 1:51 p.m.