Triple
T9152382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LuLu |
E219617
|
entity |
| Predicate | hasCapitalizationVariant |
P12011
|
FINISHED |
| Object | Lulu |
E41999
|
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: [LuLu, hasCapitalizationVariant, Lulu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lulu Context triple: [LuLu, hasCapitalizationVariant, Lulu]
-
A.
Lulu
chosen
Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
B.
Lulu Bett
Lulu Bett is the central character of Zona Gale's Pulitzer Prize-winning novel "Miss Lulu Bett," a quiet, self-effacing Midwestern woman whose constrained life and unexpected marriage spark a journey toward independence and self-realization.
-
C.
Lillete
Lillete is an alcoholic beverage brand that forms part of Pernod Ricard’s global spirits and drinks portfolio.
-
D.
Lulu Ferocity
Lulu Ferocity is a central character known for her bold, dynamic presence and fierce, fashion-forward persona in the narrative of "Pose."
-
E.
Lulu on the Bridge
Lulu on the Bridge is a 1998 romantic mystery film written and directed by Paul Auster that blends elements of noir, fantasy, and existential drama.
- 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_69ca83e25418819093c6503deeaf30de |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca96cf4548190a3a45172f0e9d0ec |
completed | April 1, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0545c5bb48190b889e6e9ef0448a5 |
completed | April 3, 2026, 11:59 p.m. |
Created at: March 30, 2026, 7:20 p.m.