Triple
T5992127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liepāja |
E133373
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Halle (Saale) |
E94413
|
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: Halle (Saale) | Statement: [Liepāja, hasTwinTown, Halle (Saale)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Halle (Saale) Context triple: [Liepāja, hasTwinTown, Halle (Saale)]
-
A.
Halle (Saale)
chosen
Halle (Saale) is a major city in the German state of Saxony-Anhalt, known as an important economic, cultural, and educational center, including being home to the Martin Luther University of Halle-Wittenberg.
-
B.
Sangerhausen
Sangerhausen is a town in the German state of Saxony-Anhalt, known for its historic mining heritage and its renowned Europa-Rosarium rose garden.
-
C.
Halle
Halle is a surname most notably borne by Morris Halle, a prominent linguist and phonologist.
-
D.
Halle
Halle is a historic city in the Belgian province of Flemish Brabant, known for its medieval architecture and the Basilica of Saint Martin, a notable pilgrimage site.
-
E.
Oranienburg
Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
- 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_69c0087010d081908bb8142342d63330 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04e8fd030819095a4f3b3d425ec21 |
completed | March 22, 2026, 8:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c10861c0bc8190b6290d7363f4264a |
completed | March 23, 2026, 9:31 a.m. |
Created at: March 22, 2026, 4:05 p.m.