Triple
T9146304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Starnberg |
E219462
|
entity |
| Predicate | hasLakesideTown |
P59883
|
FINISHED |
| Object | Tutzing |
E209639
|
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: Tutzing | Statement: [Lake Starnberg, hasLakesideTown, Tutzing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tutzing Context triple: [Lake Starnberg, hasLakesideTown, Tutzing]
-
A.
Tutzing
chosen
Tutzing is a Bavarian lakeside town in southern Germany known for its scenic location on Lake Starnberg and its role as a residential and resort community near Munich.
-
B.
Tazmalt
Tazmalt is a town and commune in northern Algeria known as a local commercial and transport hub within Béjaïa Province.
-
C.
Tagüeña
Tagüeña is a Spanish surname most notably associated with Manuel Tagüeña, a Republican military officer and physicist active during the Spanish Civil War.
-
D.
Otumba
Otumba is a town in central Mexico historically notable as the site of the Battle of Otumba during the Spanish conquest.
-
E.
Tumeremo
Tumeremo is a mining town in southeastern Venezuela known for its gold deposits and location within Bolívar State.
- 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_69ca83e121dc81909912bd66953081c5 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca917914c8190b97ca9169bbd1e5e |
completed | April 1, 2026, 5:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0482995cc81909bfc202cbab7f8a1 |
completed | April 3, 2026, 11:07 p.m. |
Created at: March 30, 2026, 7:20 p.m.