Triple
T29035519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sega Multi-Mega |
E737847
|
entity |
| Predicate | marketNameInEurope |
P170676
|
FINISHED |
| Object | Multi-Mega |
—
|
NE NERFINISHED |
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: Multi-Mega | Statement: [Sega Multi-Mega, marketNameInEurope, Multi-Mega]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marketNameInEurope Context triple: [Sega Multi-Mega, marketNameInEurope, Multi-Mega]
-
A.
marketNameInUnitedKingdom
Indicates that an entity’s market name is the one used specifically in the United Kingdom.
-
B.
marketNameInSpain
Indicates the specific name under which something (such as a product, brand, or service) is marketed in Spain.
-
C.
marketNameInUnitedStates
Indicates that an entity’s name or designation as used in the United States market is being specified.
-
D.
marketNameInNorthAmerica
Indicates the specific name under which something is marketed within the North American region.
-
E.
marketNameInJapan
Indicates the specific name under which a product, brand, or entity is marketed in Japan.
- 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_69f077efb3848190b41574e1670f6ae2 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f693ffa7908190aa4c451b16df9be6 |
completed | May 3, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69f690eb1e948190aab41a89969519a5 |
completed | May 3, 2026, 12:03 a.m. |
| PDg | Predicate description generation | batch_69f6938244648190a553b532387b812c |
completed | May 3, 2026, 12:14 a.m. |
Created at: April 28, 2026, 9:58 a.m.