Triple
T4380553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hell’s Highway |
E99117
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Veghel |
E177331
|
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: Veghel | Statement: [Hell’s Highway, passesNear, Veghel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Veghel Context triple: [Hell’s Highway, passesNear, Veghel]
-
A.
Veghel
chosen
Veghel is a town in the southern Netherlands known as an industrial and logistics hub within the province of North Brabant.
-
B.
Uithoorn
Uithoorn is a town and municipality in the province of North Holland in the Netherlands, situated along the Amstel River.
-
C.
Numansdorp
Numansdorp is a village in the western Netherlands known for its rural character and location on the island of Hoeksche Waard.
-
D.
Hardinxveld-Giessendam
Hardinxveld-Giessendam is a Dutch town and municipality known for its shipbuilding industry and location along the river Merwede in the province of South Holland.
-
E.
Winterswijk
Winterswijk is a town in the eastern Netherlands known for its rural landscape, textile-industry history, and location near the German 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_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.