Triple
T3521894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eurodollar time deposits |
E74440
|
entity |
| Predicate | typicalCounterparties |
P20464
|
FINISHED |
| Object | multinational corporations |
—
|
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: multinational corporations | Statement: [Eurodollar time deposits, typicalCounterparties, multinational corporations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCounterparties Context triple: [Eurodollar time deposits, typicalCounterparties, multinational corporations]
-
A.
tradingPartner
Indicates a relationship where two entities engage in the exchange of goods, services, or financial instruments with one another.
-
B.
hasCounterpart
Indicates that one entity corresponds to, matches, or serves as an equivalent or parallel version of another entity.
-
C.
participatingParty
chosen
Indicates that an entity is involved as a participant in a particular event, activity, or transaction.
-
D.
mainTradingPartnerCountry
Indicates the country that serves as the primary trading partner for a given entity, based on the largest or most significant volume of trade.
-
E.
frequentlyTradedAgainst
Indicates that two entities are commonly exchanged or traded with each other in a significant number of transactions over time.
- F. None of above.
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_69ad85d0c5488190a3d8e02ebd01a1aa |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc4dd6d48190a5a3f4b86c82b86c |
completed | March 8, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69adae121a048190b03825a001d21f49 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:19 p.m.