Triple
T5550286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teufelssee |
E145508
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Teufelsberg |
E137770
|
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: Teufelsberg | Statement: [Teufelssee, near, Teufelsberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teufelsberg Context triple: [Teufelssee, near, Teufelsberg]
-
A.
Teufelsberg
chosen
Teufelsberg is an artificial hill in Berlin built from World War II rubble, best known for its former U.S. listening station and panoramic city views.
-
B.
Alt-Tempelhof
Alt-Tempelhof is an underground station on Berlin’s U-Bahn network serving the Tempelhof district in the southern part of the city.
-
C.
Schöneberg Gasometer
The Schöneberg Gasometer is a historic former gas storage structure in Berlin that has become a prominent industrial landmark and event venue.
-
D.
Roter Turm
Roter Turm is a historic clock and bell tower in Halle (Saale), Germany, and one of the city’s most recognizable architectural landmarks.
-
E.
Roter Turm
Roter Turm is a historic medieval tower and prominent architectural landmark in the city center of Chemnitz, 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_69c008fb879c81909f5bfa56fadc1d46 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01fe2aef481909944bc582c1f67a4 |
completed | March 22, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0283101dc8190a52ef2edbb523e78 |
completed | March 22, 2026, 5:34 p.m. |
Created at: March 22, 2026, 3:35 p.m.