Triple
T18553855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louise Brooks |
E453448
|
entity |
| Predicate | wrote |
P2831
|
FINISHED |
| Object |
Lulu in Hollywood
Lulu in Hollywood is a memoir by silent film star Louise Brooks, offering candid reflections on her life and career in early Hollywood and European cinema.
|
E1329819
|
NE FINISHED |
How this triple was built (4 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 in Hollywood | Statement: [Louise Brooks, wrote, Lulu in Hollywood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lulu in Hollywood Context triple: [Louise Brooks, wrote, Lulu in Hollywood]
-
A.
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.
-
B.
Lulu
Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
C.
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.
-
D.
Lulu
Lulu is a fictional character best known from the Japanese film "Swallowtail Butterfly," in which she is portrayed by actress Ayumi Ito.
-
E.
Lulu
Lulu is a Scottish singer and actress best known for her powerful pop vocals and hits like "To Sir with Love" and "Shout."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lulu in Hollywood Triple: [Louise Brooks, wrote, Lulu in Hollywood]
Generated description
Lulu in Hollywood is a memoir by silent film star Louise Brooks, offering candid reflections on her life and career in early Hollywood and European cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lulu in Hollywood Target entity description: Lulu in Hollywood is a memoir by silent film star Louise Brooks, offering candid reflections on her life and career in early Hollywood and European cinema.
-
A.
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.
-
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 a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
E.
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.
- F. None of above. chosen
Provenance (5 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_69d8d388b0c881908e610a1c45b52640 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5380341548190873eeb92f86bd6fe |
completed | April 19, 2026, 8:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a049ae1d420819081fa8a691406ca65 |
completed | May 13, 2026, 3:38 p.m. |
| NEDg | Description generation | batch_6a049c6de51c8190ac839784577fa9b7 |
completed | May 13, 2026, 3:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a049d555d208190a729bac48489ed3e |
completed | May 13, 2026, 3:48 p.m. |
Created at: April 10, 2026, 11:38 a.m.