Triple
T17204425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abbottabad District |
E417562
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object | Havelian |
E271514
|
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: Havelian | Statement: [Abbottabad District, containsSettlement, Havelian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Havelian Context triple: [Abbottabad District, containsSettlement, Havelian]
-
A.
Havelian
chosen
Havelian is a town in Pakistan’s Khyber Pakhtunkhwa province, known as a local commercial center and a key junction on the Karakoram Highway.
-
B.
Haveltermade
Haveltermade is a residential district of the Dutch city of Meppel in the province of Drenthe.
-
C.
Havel
The Havel is a river in northeastern Germany that flows through Berlin and Brandenburg before joining the Elbe.
-
D.
Patočka
Patočka is a Czech surname most notably borne by the influential 20th-century philosopher Jan Patočka.
-
E.
Havel landscape
The Havel landscape is a scenic riverine region in Germany characterized by its winding waterways, lakes, and wooded shores, offering expansive natural vistas.
- 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_69d886d6ba8c819093215917b3d01689 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42db1e01c81909db0491fd9f49bed |
completed | April 19, 2026, 1:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a015fde8ba08190ae88dc9ea3366a68 |
completed | May 11, 2026, 4:49 a.m. |
Created at: April 10, 2026, 5:38 a.m.