Triple
T9064753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adolf Loos |
E217217
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Loos |
E472561
|
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: Loos | Statement: [Adolf Loos, familyName, Loos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Loos Context triple: [Adolf Loos, familyName, Loos]
-
A.
Loos
chosen
Loos is a commune in northern France that forms part of the Lille metropolitan area.
-
B.
La Hulpe
La Hulpe is a small, affluent municipality in Walloon Brabant, Belgium, known for its green surroundings and the Château de La Hulpe within the Solvay Regional Estate.
-
C.
Breda
Breda is an Italian industrial company best known for manufacturing railway rolling stock, including trains and trams used in transit systems worldwide.
-
D.
Breda
Breda is a historic city in the southern Netherlands known for its medieval architecture, former status as a military and political center, and vibrant cultural life.
-
E.
Knokke-Heist
Knokke-Heist is a Belgian coastal resort town known for its beaches, upscale tourism, and proximity to the Dutch border.
- 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_69ca83d5a7f48190b16c1e59bd43ede0 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc94bb26588190b7d6f2d70819e86f |
completed | April 1, 2026, 3:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d017a3926881909140f59c60ec3588 |
completed | April 3, 2026, 7:40 p.m. |
Created at: March 30, 2026, 7:11 p.m.