Triple
T11092158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crypt of Colònia Güell |
E262281
|
entity |
| Predicate | commissionedBy |
P27
|
FINISHED |
| Object | Eusebi Güell |
E254032
|
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: Eusebi Güell | Statement: [Crypt of Colònia Güell, commissionedBy, Eusebi Güell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eusebi Güell Context triple: [Crypt of Colònia Güell, commissionedBy, Eusebi Güell]
-
A.
Eusebi Güell
chosen
Eusebi Güell was a wealthy Catalan industrialist and politician best known as a major patron of architect Antoni Gaudí, commissioning several of his most iconic works.
-
B.
Joan Güell i Ferrer
Joan Güell i Ferrer was a prominent 19th-century Catalan industrialist and businessman who played a key role in the early industrialization of Barcelona.
-
C.
Lluís Domènech i Montaner
Lluís Domènech i Montaner was a prominent Catalan modernist architect and politician, renowned for his richly ornamented buildings in Barcelona and his influence on Catalan cultural nationalism.
-
D.
Ildefons Cerdà
Ildefons Cerdà was a 19th-century Catalan engineer and urban planner best known for designing the modern expansion plan of Barcelona.
-
E.
Gaudi
Gaudi is Habana Labs’ AI training processor designed to accelerate deep learning workloads with high performance and efficiency.
- 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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799ec6564819097624195d0cd9093 |
completed | April 9, 2026, 12:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e462e5c08c8190bba2e3c8ec82051b |
completed | April 19, 2026, 5:06 a.m. |
Created at: April 8, 2026, 9:27 p.m.