Triple
T34345436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Werne |
E881417
|
entity |
| Predicate | hasTransportConnection |
P845
|
FINISHED |
| Object |
B54 federal road
The B54 federal road is a major German highway that connects several cities in North Rhine-Westphalia and beyond, serving as an important regional transport route.
|
E2092055
|
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: B54 federal road | Statement: [Werne, hasTransportConnection, B54 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: B54 federal road Triple: [Werne, hasTransportConnection, B54 federal road]
Generated description
The B54 federal road is a major German highway that connects several cities in North Rhine-Westphalia and beyond, serving as an important regional transport 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_69f349bc55e881908c8e338ef76b0043 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f713ef2cb48190bf20608c3af3897d |
completed | May 3, 2026, 9:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a36f9e544d48190b29ac096fba90d87 |
completed | June 20, 2026, 8:36 p.m. |
| NEDg | Description generation | batch_6a36fb5b18b88190a3867c5831918de7 |
completed | June 20, 2026, 8:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a36fc2b7ad8819080f589c461aa960a |
completed | June 20, 2026, 8:46 p.m. |
Created at: May 1, 2026, 1:58 a.m.