Triple
T10367447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Human Traffic |
E244291
|
entity |
| Predicate | mainCharacter |
P1183
|
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: [Human Traffic, mainCharacter, Lulu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lulu Context triple: [Human Traffic, mainCharacter, 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.
Lilli
Lilli is a feminine given name, often used in German-speaking and other European countries, and famously borne by the actress Lilli Palmer.
-
E.
Lulu Ferocity
Lulu Ferocity is a central character known for her bold, dynamic presence and fierce, fashion-forward persona in the narrative of "Pose."
- 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e96fd6f081908f630a16106996d9 |
completed | April 7, 2026, 11:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d79546b078819089dec7628c95a681 |
completed | April 9, 2026, 12:02 p.m. |
Created at: April 6, 2026, noon