Triple
T2352049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Black Dahlia |
E47468
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Nu Image
Nu Image is a film production company known for producing a wide range of genre movies, including action, thriller, and crime dramas.
|
E259306
|
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: Nu Image | Statement: [The Black Dahlia, productionCompany, Nu Image]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nu Image Context triple: [The Black Dahlia, productionCompany, Nu Image]
-
A.
Neo
Neo is the protagonist of the science fiction film series "The Matrix," a hacker who becomes humanity's prophesied savior within a simulated reality.
-
B.
Nuk
Nuk is a well-known baby care brand specializing in products like bottles, pacifiers, and accessories designed to support natural oral development.
-
C.
Tupian
Tupian is a major indigenous language family of South America, encompassing numerous related languages spoken primarily in Brazil and neighboring regions.
-
D.
NuMachine
NuMachine was an early 1980s experimental workstation computer project at MIT that pioneered the NuBus expansion bus architecture.
-
E.
Nuska
Nuska is a Mesopotamian god of fire and light, often serving as a divine vizier and attendant to major deities in the Sumerian and Akkadian pantheons.
- 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: Nu Image Triple: [The Black Dahlia, productionCompany, Nu Image]
Generated description
Nu Image is a film production company known for producing a wide range of genre movies, including action, thriller, and crime dramas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nu Image Target entity description: Nu Image is a film production company known for producing a wide range of genre movies, including action, thriller, and crime dramas.
-
A.
Neo
Neo is the protagonist of the science fiction film series "The Matrix," a hacker who becomes humanity's prophesied savior within a simulated reality.
-
B.
Nuk
Nuk is a well-known baby care brand specializing in products like bottles, pacifiers, and accessories designed to support natural oral development.
-
C.
Tupian
Tupian is a major indigenous language family of South America, encompassing numerous related languages spoken primarily in Brazil and neighboring regions.
-
D.
NuMachine
NuMachine was an early 1980s experimental workstation computer project at MIT that pioneered the NuBus expansion bus architecture.
-
E.
Nuska
Nuska is a Mesopotamian god of fire and light, often serving as a divine vizier and attendant to major deities in the Sumerian and Akkadian pantheons.
- 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_69a88a1b678c8190bce986922ba60ce0 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc6f8ff548190b07505310e2bf0b9 |
completed | March 7, 2026, 6:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae9631c9a481909a3051ac06afdca7 |
completed | March 9, 2026, 9:43 a.m. |
| NEDg | Description generation | batch_69ae97f3e3348190824617b882a98146 |
completed | March 9, 2026, 9:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae98f57268819086c14006df46b794 |
completed | March 9, 2026, 9:55 a.m. |
Created at: March 4, 2026, 7:54 p.m.