Triple
T23448087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Colonsay |
E565596
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object | Pau, France |
—
|
NE NERFINISHED |
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: Pau, France | Statement: [Lord Colonsay, placeOfDeath, Pau, France]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pau, France Context triple: [Lord Colonsay, placeOfDeath, Pau, France]
-
A.
Pau
Pau is the Catalan given name of the renowned cellist and conductor Pablo Casals.
-
B.
Pau
Pau is a traditional Hawaiian skirt-like garment, often made from tapa cloth or ti leaves and worn in hula and other cultural practices.
-
C.
Pau
chosen
Pau is a historic city in southwestern France, known as the capital of the Pyrénées-Atlantiques department and for its scenic location near the Pyrenees mountains.
-
D.
City of Pau
The City of Pau is a commune in southwestern France, known as the historic capital of Béarn and for its scenic location overlooking the Pyrenees.
-
E.
Montpellier, France
Montpellier, France is a historic and vibrant city in southern France near the Mediterranean coast, known for its medieval architecture, large student population, and role as a regional cultural and economic center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64b27988190b4722425da964407 |
completed | April 29, 2026, 6:33 a.m. |
Created at: April 17, 2026, 5:52 p.m.