Triple
T12685011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Otto Lilienthal |
E303044
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lilienthal |
E303044
|
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: Lilienthal | Statement: [Otto Lilienthal, familyName, Lilienthal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lilienthal Context triple: [Otto Lilienthal, familyName, Lilienthal]
-
A.
Lilienthal
chosen
Lilienthal is a German-origin surname borne by various notable individuals, including figures in aviation, science, and public service.
-
B.
Planegg
Planegg is a municipality in the district of Munich in Bavaria, Germany, known for its scenic location along the Würm River and its proximity to the city of Munich.
-
C.
Delitzsch
Delitzsch is a historic town in the German state of Saxony, known for its well-preserved medieval center and regional administrative role.
-
D.
Lippendorf
Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
-
E.
Biesenthal
Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961d7cd4c81909521839ef5859799 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f684e2292c8190bffb3a8b6e15029c |
completed | May 2, 2026, 11:12 p.m. |
Created at: April 9, 2026, 5:21 p.m.