Triple
T31508809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muse |
E803888
|
entity |
| Predicate | oppositeSettlementCountry |
P185328
|
FINISHED |
| Object | China |
—
|
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: China | Statement: [Muse, oppositeSettlementCountry, China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oppositeSettlementCountry Context triple: [Muse, oppositeSettlementCountry, China]
-
A.
oppositeBankCountry
Indicates that two entities are located on opposing banks of the same river or waterway, each in a different country.
-
B.
counterpartyCountry
Indicates the country associated with the other party involved in a transaction, agreement, or relationship.
-
C.
oppositeTownCountry
Indicates that two locations are situated in opposing or contrasting town and country settings, such that one is urban while the other is rural.
-
D.
settlementCountryRegion
Indicates the country or broader geographic region in which a settlement is located or administered.
-
E.
typicalSettlementCountry
Indicates the country in which an entity’s financial transactions, obligations, or trades are most commonly settled.
- 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_69f348ceb0a48190ae7feca263b6296c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f7be53890081909b1d93f30a8f31c6 |
completed | May 3, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69f7bccacbac8190978976324c67db28 |
completed | May 3, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69f7be520f148190ba200bf3dbf40656 |
completed | May 3, 2026, 9:29 p.m. |
Created at: April 30, 2026, 9:48 p.m.