Triple
T11171518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Judge Turpin |
E264283
|
entity |
| Predicate | notableSong |
P4
|
FINISHED |
| Object | Johanna (Judge Turpin’s version) |
E366888
|
NE FINISHED |
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: Johanna (Judge Turpin’s version) | Statement: [Judge Turpin, notableSong, Johanna (Judge Turpin’s version)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Johanna (Judge Turpin’s version) Context triple: [Judge Turpin, notableSong, Johanna (Judge Turpin’s version)]
-
A.
Mathilda
Mathilda is the young girl in the film "Léon: The Professional" who becomes the protégé of a solitary hitman after her family is murdered.
-
B.
Mathilda
Mathilda is the middle name of Elivera Mathilda Carlson Doud, the wife of former U.S. President Dwight D. Eisenhower.
-
C.
Johanna
Johanna is the given name of Johanna Spyri, the Swiss author best known for creating the classic children's novel "Heidi."
-
D.
Johanna
chosen
"Johanna" is a recurring, lyrically poignant love song from Stephen Sondheim's musical *Sweeney Todd: The Demon Barber of Fleet Street*.
-
E.
Johanna
Johanna is the birth name of Magda Goebbels, the wife of Nazi propaganda minister Joseph Goebbels and a prominent figure in Nazi Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e89660208190b1d9e91529f5d246 |
completed | April 9, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e463b155a08190b361b38a39d25b1f |
completed | April 19, 2026, 5:10 a.m. |
Created at: April 8, 2026, 9:29 p.m.