Triple
T17104623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barcelona Chair |
E415064
|
entity |
| Predicate | hasCounterfeitIssues |
P38316
|
FINISHED |
| Object | widely copied and reproduced |
—
|
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: widely copied and reproduced | Statement: [Barcelona Chair, hasCounterfeitIssues, widely copied and reproduced]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCounterfeitIssues Context triple: [Barcelona Chair, hasCounterfeitIssues, widely copied and reproduced]
-
A.
isSubjectToCounterfeiting
chosen
Indicates that something is vulnerable to being illegally imitated, forged, or reproduced in order to deceive.
-
B.
hasIssueWith
Indicates that one entity experiences a problem, conflict, or concern related to another entity.
-
C.
issuesBanknotes
Indicates that an entity (typically a central bank or monetary authority) produces and puts banknotes into official circulation as legal tender.
-
D.
oftenIssuedAs
Indicates that one entity is frequently released, published, or distributed in the form of another entity.
-
E.
notLegalTenderIn
Indicates that a form of money is not officially recognized as acceptable payment within a specified jurisdiction or region.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc2591a881909c5f4f7db47f4d6c |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d6b1b988190a8d6b6fe78c35e59 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:35 a.m.