Triple
T11110501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Welkenraedt |
E262741
|
entity |
| Predicate | sharesBorderWith |
P224
|
FINISHED |
| Object | Baelen |
E263274
|
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: Baelen | Statement: [Welkenraedt, sharesBorderWith, Baelen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baelen Context triple: [Welkenraedt, sharesBorderWith, Baelen]
-
A.
Baelen
chosen
Baelen is a small municipality in the province of Liège in eastern Belgium, near the German border.
-
B.
Leira
Leira is a river in southeastern Norway that serves as one of the tributaries feeding into the country’s longest river, the Glomma.
-
C.
Laena Velaryon
Laena Velaryon is a noblewoman of House Velaryon with Valyrian dragonrider heritage, known from George R. R. Martin’s "A Song of Ice and Fire" universe and its adaptations.
-
D.
Talisa Maegyr
Talisa Maegyr is a healer and noblewoman in the A Song of Ice and Fire universe, known for her romantic relationship and marriage to Robb Stark in the television adaptation Game of Thrones.
-
E.
Baelor
"Baelor" is a pivotal and emotionally devastating episode of Game of Thrones’ first season, best known for its shocking execution of a major character and the dramatic turning point it creates in the series.
- 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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79a6964508190b679303d3b3a4fd6 |
completed | April 9, 2026, 12:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d759bc88190b670c373f3647a41 |
completed | April 19, 2026, 1:18 a.m. |
Created at: April 8, 2026, 9:27 p.m.