Triple
T4380554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hell’s Highway |
E99117
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Uden |
E131040
|
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: Uden | Statement: [Hell’s Highway, passesNear, Uden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uden Context triple: [Hell’s Highway, passesNear, Uden]
-
A.
Uden
chosen
Uden is a town in the southern Netherlands known for its location in the province of North Brabant and its proximity to nature reserves and regional industry.
-
B.
Unna
Unna is a town in the German state of North Rhine-Westphalia, known historically as a regional trading center near Dortmund.
-
C.
Undy
Undy is a village in Monmouthshire, Wales, situated near the town of Caldicot in the southeast of the country.
-
D.
Vanløse
Vanløse is a residential district in the western part of Copenhagen, Denmark, known for its local shopping streets, green areas, and strong public transport connections.
-
E.
Ödeen
Ödeen is a Swedish surname most notably borne by individuals such as Kjell Ödeen.
- 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_69b3454ea8f48190a49c2436624d6ef6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35243036481909fb0a001c3cb1ff2 |
completed | March 12, 2026, 11:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e51ff9188190aa4581d451feaafd |
completed | March 14, 2026, 10:45 p.m. |
Created at: March 12, 2026, 11:18 p.m.