Triple
T13744120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Black |
E330169
|
entity |
| Predicate | collaboratedWith |
P435
|
FINISHED |
| Object | Lulu |
unclear NED1
|
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: Lulu | Statement: [Don Black, collaboratedWith, Lulu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lulu Context triple: [Don Black, collaboratedWith, Lulu]
-
A.
Lulu
Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
B.
Lulu
Lulu is a central character in the 1999 British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
-
C.
Lulu
Lulu is a fictional character best known from the Japanese film "Swallowtail Butterfly," in which she is portrayed by actress Ayumi Ito.
-
D.
Lulu
Lulu is an avant-garde opera by Alban Berg, a key work of early 20th-century modernist music associated with the Second Viennese School.
-
E.
Lulu
Lulu is the central character in Harold Pinter’s play "The Birthday Party," around whom the play’s unsettling and ambiguous events revolve.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de020855ec8190a60fa1cb761f2e68 |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a8501890819081152bc2e9c06836 |
completed | May 3, 2026, 7:56 p.m. |
Created at: April 9, 2026, 10:08 p.m.