Triple
T1748186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Conjuring |
E38380
|
entity |
| Predicate | hasSpinOff |
P7226
|
FINISHED |
| Object |
Annabelle
Annabelle is a horror film centered on a malevolent possessed doll, serving as a spin-off and prequel within The Conjuring cinematic universe.
|
E195774
|
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: Annabelle | Statement: [The Conjuring, hasSpinOff, Annabelle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Annabelle Context triple: [The Conjuring, hasSpinOff, Annabelle]
-
A.
Annabella
Annabella was a French film actress of the 1930s and 1940s, known for her work in both European and Hollywood cinema.
-
B.
Apparition
Apparition was an independent American film distribution company known for releasing prestige and arthouse films in the late 2000s.
-
C.
The Boogeyman
"The Boogeyman" is a horror short story by Stephen King about a man recounting terrifying encounters with a child-killing monster to a psychiatrist.
-
D.
Boo
Boo is a suburban district and island area in the Stockholm archipelago, located within Nacka Municipality in Sweden.
-
E.
Boo
Boo is a statically typed, Python-inspired programming language for the .NET platform that was once used as a primary scripting option in the Unity game engine.
- 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: Annabelle Triple: [The Conjuring, hasSpinOff, Annabelle]
Generated description
Annabelle is a horror film centered on a malevolent possessed doll, serving as a spin-off and prequel within The Conjuring cinematic universe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Annabelle Target entity description: Annabelle is a horror film centered on a malevolent possessed doll, serving as a spin-off and prequel within The Conjuring cinematic universe.
-
A.
Annabella
Annabella was a French film actress of the 1930s and 1940s, known for her work in both European and Hollywood cinema.
-
B.
Apparition
Apparition was an independent American film distribution company known for releasing prestige and arthouse films in the late 2000s.
-
C.
The Boogeyman
"The Boogeyman" is a horror short story by Stephen King about a man recounting terrifying encounters with a child-killing monster to a psychiatrist.
-
D.
Boo
Boo is a suburban district and island area in the Stockholm archipelago, located within Nacka Municipality in Sweden.
-
E.
Boo
Boo is a statically typed, Python-inspired programming language for the .NET platform that was once used as a primary scripting option in the Unity game engine.
- 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa63ecda0c819091f81942a5bde31d |
completed | March 6, 2026, 5:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0e21e58819082943212bd725581 |
completed | March 8, 2026, 4:16 p.m. |
| NEDg | Description generation | batch_69ada1a2122481909c7a3470e090af17 |
completed | March 8, 2026, 4:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ada23515d08190833ad1a35bb7a265 |
completed | March 8, 2026, 4:22 p.m. |
Created at: March 4, 2026, 7:31 p.m.