Triple
T16850264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Death Note (2017 film) |
E409655
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
L
L is a brilliant and enigmatic world-class detective from the Death Note series, known for his eccentric behavior, unmatched deductive skills, and relentless pursuit of justice.
|
E1236727
|
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: L | Statement: [Death Note (2017 film), mainCharacter, L]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: L Context triple: [Death Note (2017 film), mainCharacter, L]
-
A.
L
L is the enigmatic, emotionally complex protagonist of Hanne Ørstavik’s novel "Love," whose inner life and perspective drive the story’s exploration of isolation and longing.
-
B.
L
The L is a Chicago 'L' rapid transit line that serves the city’s West Side and western suburbs as part of the Chicago Transit Authority system.
-
C.
L
L is the vehicle registration code used on license plates for the German city and district of Leipzig.
-
D.
L
L is a light rail service line in San Francisco’s Muni Metro system.
-
E.
L
The L is a New York City Subway line that runs crosstown through Manhattan and into Brooklyn, including service to neighborhoods such as Canarsie.
- 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: L Triple: [Death Note (2017 film), mainCharacter, L]
Generated description
L is a brilliant and enigmatic world-class detective from the Death Note series, known for his eccentric behavior, unmatched deductive skills, and relentless pursuit of justice.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: L Target entity description: L is a brilliant and enigmatic world-class detective from the Death Note series, known for his eccentric behavior, unmatched deductive skills, and relentless pursuit of justice.
-
A.
L
L is the enigmatic, emotionally complex protagonist of Hanne Ørstavik’s novel "Love," whose inner life and perspective drive the story’s exploration of isolation and longing.
-
B.
L
L is a light rail service line in San Francisco’s Muni Metro system.
-
C.
L
The L is a New York City Subway line that runs crosstown through Manhattan and into Brooklyn, including service to neighborhoods such as Canarsie.
-
D.
L
L is a Swedish liberal political party known for advocating individual freedom, social responsibility, and pro-European policies.
-
E.
L
The L is a Chicago 'L' rapid transit line that serves the city’s West Side and western suburbs as part of the Chicago Transit Authority system.
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b378dda48190ab81d75f2cfe3ab3 |
completed | April 18, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bb1f02648190937c692af83843dc |
completed | May 10, 2026, 5:06 p.m. |
| NEDg | Description generation | batch_6a00bbc80d54819092de4ee363508b49 |
completed | May 10, 2026, 5:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00bc633abc8190a86808986ba294ec |
completed | May 10, 2026, 5:12 p.m. |
Created at: April 10, 2026, 5:24 a.m.