Triple
T18016022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | COCO |
E431000
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Common Objects in Context |
—
|
NE NERFINISHED |
How this triple was built (3 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: Common Objects in Context | Statement: [COCO, fullName, Common Objects in Context]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Common Objects in Context Context triple: [COCO, fullName, Common Objects in Context]
-
A.
“Reasoning with Arbitrary Objects”
“Reasoning with Arbitrary Objects” is a philosophical work by Kit Fine that develops a formal framework for reasoning about arbitrary objects in logic and metaphysics.
-
B.
A Theory of Objects
A Theory of Objects is a foundational book in theoretical computer science that develops a rigorous mathematical framework for understanding object-oriented programming and type systems.
-
C.
The System of Objects
The System of Objects is a seminal 1968 work of critical theory by Jean Baudrillard that analyzes consumer goods as signs within a broader system of social meaning and everyday life.
-
D.
Scene Completion Using Millions of Photographs
"Scene Completion Using Millions of Photographs" is a seminal computer vision and graphics paper that introduced a data-driven method for automatically filling in missing regions of images by searching a massive online photo collection for visually compatible patches.
-
E.
Learning to See
"Learning to See" is an autobiographical essay by Eudora Welty that reflects on how her early experiences and observations shaped her development as a writer.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Common Objects in Context Target entity description: Common Objects in Context is a large-scale image recognition, segmentation, and captioning dataset widely used as a benchmark in computer vision research.
-
A.
“Reasoning with Arbitrary Objects”
“Reasoning with Arbitrary Objects” is a philosophical work by Kit Fine that develops a formal framework for reasoning about arbitrary objects in logic and metaphysics.
-
B.
A Theory of Objects
A Theory of Objects is a foundational book in theoretical computer science that develops a rigorous mathematical framework for understanding object-oriented programming and type systems.
-
C.
The System of Objects
The System of Objects is a seminal 1968 work of critical theory by Jean Baudrillard that analyzes consumer goods as signs within a broader system of social meaning and everyday life.
-
D.
Scene Completion Using Millions of Photographs
"Scene Completion Using Millions of Photographs" is a seminal computer vision and graphics paper that introduced a data-driven method for automatically filling in missing regions of images by searching a massive online photo collection for visually compatible patches.
-
E.
Learning to See
"Learning to See" is an autobiographical essay by Eudora Welty that reflects on how her early experiences and observations shaped her development as a writer.
- F. None of above. chosen
Provenance (2 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.