Triple
T5720634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waterland |
E126134
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object | Marken |
E126132
|
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: Marken | Statement: [Waterland, containsSettlement, Marken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marken Context triple: [Waterland, containsSettlement, Marken]
-
A.
Marken
chosen
Marken is a small, picturesque former island village in the Netherlands known for its traditional wooden houses, fishing heritage, and distinctive cultural character.
-
B.
Brandbu
Brandbu is a village in Gran Municipality in Innlandet county, Norway, known as a local commercial and service center in the Hadeland district.
-
C.
Brand
Brand is a key astronaut and scientist in the film "Interstellar," serving as one of the central figures in humanity's mission to find a new habitable world.
-
D.
Brand
Brand is a verse drama by Norwegian playwright Henrik Ibsen that explores the moral absolutism and tragic consequences of its idealistic priest protagonist.
-
E.
Shell brand
Shell brand is a global energy and petrochemicals company best known for its distinctive red-and-yellow seashell emblem and extensive network of fuel stations worldwide.
- 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_69c0082e3d548190950169847b43043b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c024e328e08190a67e845b296e34e9 |
completed | March 22, 2026, 5:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a7db0788190b4a5e7b5d9c94588 |
completed | March 22, 2026, 9:09 p.m. |
Created at: March 22, 2026, 3:46 p.m.