Triple
T12216297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | James Cobb |
E291093
|
entity |
| Predicate | appearsIn |
P795
|
FINISHED |
| Object | Inception |
E26277
|
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: Inception | Statement: [James Cobb, appearsIn, Inception]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Inception Context triple: [James Cobb, appearsIn, Inception]
-
A.
Inception
chosen
Inception is a 2010 science fiction heist film directed by Christopher Nolan that explores dream manipulation and shared subconscious worlds.
-
B.
Inception v1
Inception v1 is the original version of Google’s Inception deep convolutional neural network architecture, introduced for efficient and accurate image classification in the 2014 GoogLeNet model.
-
C.
Memento
Memento is a 2000 neo-noir psychological thriller film written and directed by Christopher Nolan, renowned for its non-linear narrative structure that explores memory, identity, and perception.
-
D.
Memento
Memento is a behavioral design pattern that captures and externalizes an object's internal state so it can be restored later without violating encapsulation.
-
E.
Inception v2
Inception v2 is an improved version of Google’s Inception convolutional neural network architecture that enhances accuracy and efficiency through refined module design and training techniques.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c9419d48190b0037fe8edc681c4 |
completed | April 10, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63ee89b28819095e2e5df8acbcb22 |
completed | May 2, 2026, 6:14 p.m. |
Created at: April 8, 2026, 9:51 p.m.