Triple
T7049010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pablo Casals |
E163716
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Prades |
E152411
|
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: Prades | Statement: [Pablo Casals, residence, Prades]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prades Context triple: [Pablo Casals, residence, Prades]
-
A.
Prades
chosen
Prades is a small town in southern France known for its picturesque setting in the Pyrenees and its cultural and historical heritage.
-
B.
Bonnieux
Bonnieux is a picturesque hilltop village in southeastern France’s Provence region, known for its historic stone houses, terraced streets, and panoramic views over the Luberon valley.
-
C.
Lacanau
Lacanau is a coastal resort town in southwestern France known for its Atlantic beaches, surfing, and large freshwater lake.
-
D.
Camprodon
Camprodon is a small town in the Catalan Pyrenees of northeastern Spain, known for its scenic mountain setting and historic Romanesque architecture.
-
E.
Saint-Priest
Saint-Priest is a suburban commune in eastern France that forms part of the metropolitan area of Lyon.
- 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_69c6885f598c8190b6b6495c59d8d962 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e24d5e8c8190b37e56107e6da8ab |
completed | March 27, 2026, 8:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7888497b08190b1f2aa686e8e30c1 |
completed | March 28, 2026, 7:51 a.m. |
Created at: March 27, 2026, 2:37 p.m.