Triple
T15284327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bourbon-Parma |
E365354
|
entity |
| Predicate | seat |
P75
|
FINISHED |
| Object | Colorno |
E1136664
|
NE 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: Colorno | Statement: [Bourbon-Parma, seat, Colorno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colorno Context triple: [Bourbon-Parma, seat, Colorno]
-
A.
Colorno
chosen
Colorno is a town in the Emilia-Romagna region of northern Italy, historically notable for its grand ducal palace and its past role within the Duchy of Parma and Piacenza.
-
B.
Borouge
Borouge is a leading petrochemicals company based in the United Arab Emirates, specializing in the production of polyolefins for packaging, infrastructure, and industrial applications.
-
C.
Bordo
Bordo is the popular nickname for FK Sarajevo, a prominent Bosnian football club known for its maroon team colors.
-
D.
Blau
The Blau is a small river in the German state of Baden-Württemberg that flows through the city of Blaustein before joining the Danube.
-
E.
Maroon
"Maroon" is a moody, synth-pop song by Taylor Swift that reflects on a past romance with vivid, color-themed imagery.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00e53c9588190a6cb61ac8805c706 |
completed | April 15, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef798a588190981c77e6f4c6be78 |
completed | May 9, 2026, 8:25 a.m. |
Created at: April 10, 2026, 3:15 a.m.