Triple
T5021378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franeker |
E112857
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Harlingen |
E89980
|
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: Harlingen | Statement: [Franeker, locatedNear, Harlingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harlingen Context triple: [Franeker, locatedNear, Harlingen]
-
A.
Harlingen
chosen
Harlingen is a historic port city in the Dutch province of Friesland, known for its maritime heritage and traditional canalside architecture.
-
B.
Harlingen
Harlingen is a mid-sized city in the Rio Grande Valley of South Texas known as a regional hub for transportation, healthcare, and commerce near the U.S.–Mexico border.
-
C.
Vienenburg
Vienenburg is a district of Goslar in Lower Saxony, Germany, known for its historic town center and proximity to the Harz Mountains.
-
D.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
E.
Soest
Soest is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
- 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_69bd4435c2f48190be593158cbfcf8a3 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73656edc8190b802ad38d9552b58 |
completed | March 20, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be927f4ad0819096826f6cb141c90b |
completed | March 21, 2026, 12:43 p.m. |
Created at: March 20, 2026, 1:36 p.m.