Triple
T20200101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Egg an der Günz |
E493190
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Wolfgang Walter
Wolfgang Walter is a German local politician who serves as the mayor of the Bavarian municipality Egg an der Günz.
|
E2002649
|
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: Wolfgang Walter | Statement: [Egg an der Günz, hasMayor, Wolfgang Walter]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Wolfgang Walter Triple: [Egg an der Günz, hasMayor, Wolfgang Walter]
Generated description
Wolfgang Walter is a German local politician who serves as the mayor of the Bavarian municipality Egg an der Günz.
Provenance (5 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66d8d01648190b1b3a6e03f0258d8 |
completed | April 20, 2026, 6:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3056d7b7908190bfec44693723db23 |
completed | June 15, 2026, 7:47 p.m. |
| NEDg | Description generation | batch_6a31af0dd4b48190be2aa9c952a9aff6 |
completed | June 16, 2026, 8:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a31bab61f508190a4bfde1478397f6c |
completed | June 16, 2026, 9:05 p.m. |
Created at: April 11, 2026, 11:37 p.m.