Triple
T15875045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilhelmstadt |
E384930
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Gatow |
E384682
|
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: Gatow | Statement: [Wilhelmstadt, borders, Gatow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gatow Context triple: [Wilhelmstadt, borders, Gatow]
-
A.
Gatow
chosen
Gatow is a village-like locality in southwestern Berlin known for its former airfield and proximity to the Havel River and surrounding green spaces.
-
B.
Schkopau
Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
-
C.
Wandlitz
Wandlitz is a municipality in the German state of Brandenburg, known for its lakes, forests, and proximity to Berlin.
-
D.
Teterow
Teterow is a small historic town in northeastern Germany known for its medieval architecture and location in the Mecklenburg Lake District.
-
E.
Degendorf
Degendorf is a locality within the Bavarian town and district of Lichtenfels in Germany.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e155fcffbc8190ba6d133107b83a7f |
completed | April 16, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f7f439c8190b4bcd84e35aa291e |
completed | May 10, 2026, 6:02 a.m. |
Created at: April 10, 2026, 4:51 a.m.