Triple
T20165743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ricoh 2C02 PPU |
E491820
|
entity |
| Predicate | nameTableCount |
P138925
|
FINISHED |
| Object | 2 internal name tables |
—
|
LITERAL 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: 2 internal name tables | Statement: [Ricoh 2C02 PPU, nameTableCount, 2 internal name tables]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nameTableCount Context triple: [Ricoh 2C02 PPU, nameTableCount, 2 internal name tables]
-
A.
numberOfNames
Indicates the count of distinct names associated with a given entity.
-
B.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
C.
titleCount
Indicates the number of distinct titles associated with an entity within a given context.
-
D.
numberOfEntities
Indicates the total count of distinct entities involved in or associated with a given context or situation.
-
E.
numberPerCounty
Indicates the quantity or count of something associated with each individual county.
- F. None of above. chosen
Provenance (4 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e668442d2c81908bb1a0fac9895b5e |
completed | April 20, 2026, 5:54 p.m. |
| PD | Predicate disambiguation | batch_69e55b0c11cc8190836d1eee5945f000 |
completed | April 19, 2026, 10:45 p.m. |
| PDg | Predicate description generation | batch_69e56700b1a08190ace53cf95827d72d |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 11, 2026, 11:35 p.m.