Triple
T10293932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North American market |
E241433
|
entity |
| Predicate | hasTradeCharacteristic |
P92951
|
FINISHED |
| Object | high intra-regional trade flows |
—
|
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: high intra-regional trade flows | Statement: [North American market, hasTradeCharacteristic, high intra-regional trade flows]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTradeCharacteristic Context triple: [North American market, hasTradeCharacteristic, high intra-regional trade flows]
-
A.
hasTrade
Indicates a relationship where one entity engages in or maintains a commercial exchange or trading activity with another entity.
-
B.
catalogCharacteristic
Indicates that a catalog has a specific characteristic or attribute associated with it.
-
C.
hasRetailCharacteristic
Indicates that an entity possesses a specific attribute, feature, or quality relevant to retail contexts (such as pricing, packaging, or point-of-sale properties).
-
D.
dataCharacteristic
Indicates that one entity specifies a property, attribute, or feature that characterizes a given piece of data.
-
E.
hasBankCharacteristic
Indicates that a bank possesses a particular attribute, feature, or quality.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2d46fb08190b7694290692e47dc |
completed | April 7, 2026, 9:48 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f35e548190be3b4d92d65d2d20 |
completed | April 7, 2026, 9:44 a.m. |
| PDg | Predicate description generation | batch_69d4d29d7cf08190acd70cee634c5cdb |
completed | April 7, 2026, 9:47 a.m. |
Created at: April 6, 2026, 11:42 a.m.