Triple
T3134137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amsterdam–Rhine Canal |
E65486
|
entity |
| Predicate | endPoint |
P390
|
FINISHED |
| Object | Tiel |
E94139
|
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: Tiel | Statement: [Amsterdam–Rhine Canal, endPoint, Tiel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tiel Context triple: [Amsterdam–Rhine Canal, endPoint, Tiel]
-
A.
Tiel
chosen
Tiel is a historic Dutch city situated along the River Waal, known for its fruit cultivation and role as a regional trade center in the province of Gelderland.
-
B.
Tachov
Tachov is a town in western Czechia that serves as an administrative center and local hub within the Plzeň Region.
-
C.
Turek
Turek is a town in central Poland known historically for its textile industry and its location in the Greater Poland region.
-
D.
Tuineje
Tuineje is a coastal municipality on the island of Fuerteventura in Spain’s Canary Islands, known for its rural landscapes, beaches, and traditional Canarian culture.
-
E.
Tura
Tura is a district in southern Cairo, Egypt, historically known for its limestone quarries used in ancient Egyptian monuments.
- 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_69ad8581c25c8190b0d85ba9b9baa531 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada562540081908627950dd0b56a1e |
completed | March 8, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f84d8288190b1f48fa0f5c10773 |
completed | March 12, 2026, 12:57 a.m. |
Created at: March 8, 2026, 3:05 p.m.