Triple
T10087088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Polykleitos |
E215248
|
entity |
| Predicate | authoredWork |
P4
|
FINISHED |
| Object | Canon |
E567300
|
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: Canon | Statement: [Polykleitos, authoredWork, Canon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Canon Context triple: [Polykleitos, authoredWork, Canon]
-
A.
Canon
chosen
Canon is a structured set of hymns or chants used in Eastern Christian liturgical services, particularly within the Orthodox tradition.
-
B.
Canon Inc.
Canon Inc. is a Japanese multinational corporation renowned for its imaging and optical products, including cameras, camcorders, printers, and related equipment.
-
C.
Canon PIXMA
Canon PIXMA is a line of consumer and small-office inkjet printers from Canon known for combining high-quality photo printing with versatile document printing and scanning features.
-
D.
Ricoh
Ricoh is a Japanese multinational imaging and electronics company best known for its cameras, printers, copiers, and office equipment solutions.
-
E.
Epson
Epson is a Japanese electronics company best known for manufacturing printers, imaging equipment, and related information technology devices.
- 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_69ca83a1eed081908b2e9580f2ebeea7 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd04745b48190a77c422eb76b6660 |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b68b4dbc8190b0ada78fb29feffd |
completed | April 5, 2026, 7:22 p.m. |
Created at: March 30, 2026, 9:01 p.m.