Triple
T15394562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marian Senger "Cichy" |
E368138
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object | Senger |
E407658
|
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: Senger | Statement: [Marian Senger "Cichy", hasFamilyName, Senger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Senger Context triple: [Marian Senger "Cichy", hasFamilyName, Senger]
-
A.
Senger
chosen
Senger is a variant form of the surname Singer, commonly found in German-speaking regions.
-
B.
Spangenberg
Spangenberg is a small town in Germany, historically situated within the region of Westphalia.
-
C.
Sorge
Sorge is a river in the German state of Schleswig-Holstein that serves as one of the tributaries feeding into the Eider River.
-
D.
Sorge
Sorge is a small village in the Harz region of Saxony-Anhalt, Germany, known historically as a former border settlement in the inner-German division.
-
E.
Sautter
Sautter is a surname, likely a spelling variant of "Sutter," borne by various individuals and families of European origin.
- 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_69d85a1551a08190ba2caea7cd51c639 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e8ac79081908ac79c0b3e7587ff |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff13523f548190beafd130f8741465 |
completed | May 9, 2026, 10:58 a.m. |
Created at: April 10, 2026, 3:19 a.m.