Triple
T32340738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Essenbach |
E826305
|
entity |
| Predicate | hasTransportConnection |
P845
|
FINISHED |
| Object |
B15 federal road
The B15 federal road is a major German highway in Bavaria that connects several towns and cities, including Essenbach, and serves as an important regional traffic route.
|
E2001239
|
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: B15 federal road | Statement: [Essenbach, hasTransportConnection, B15 federal road]
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: B15 federal road Triple: [Essenbach, hasTransportConnection, B15 federal road]
Generated description
The B15 federal road is a major German highway in Bavaria that connects several towns and cities, including Essenbach, and serves as an important regional traffic route.
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_69f34913d9048190befaa634025232be |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6be20cd6c8190b365c130d0a286e7 |
completed | May 3, 2026, 3:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a30572ca9e081908939473a0b8510c8 |
completed | June 15, 2026, 7:49 p.m. |
| NEDg | Description generation | batch_6a30581f4878819083d6e510e2ca386e |
completed | June 15, 2026, 7:53 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3058d36d9c8190bec0ef32cf26c84f |
completed | June 15, 2026, 7:56 p.m. |
Created at: May 1, 2026, 12:48 a.m.