Triple
T34478958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rychlebské Mountains |
E885125
|
entity |
| Predicate | hasTouristCenter |
P2976
|
FINISHED |
| Object |
Černá Voda
Černá Voda is a Czech village known as a popular gateway and base for outdoor recreation in the Rychlebské Mountains.
|
E2098830
|
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: Černá Voda | Statement: [Rychlebské Mountains, hasTouristCenter, Černá Voda]
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: Černá Voda Triple: [Rychlebské Mountains, hasTouristCenter, Černá Voda]
Generated description
Černá Voda is a Czech village known as a popular gateway and base for outdoor recreation in the Rychlebské Mountains.
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_69f349c947fc81909d30b53c194d6ea1 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71ccc90b081908ae1b9a5dcb69d7b |
completed | May 3, 2026, 10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a372135fcb8819096033e159f5db7ac |
completed | June 20, 2026, 11:24 p.m. |
| NEDg | Description generation | batch_6a37221583e48190a3dbe0dc7ad27453 |
completed | June 20, 2026, 11:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3722b5be848190a4d017d80dea0ad6 |
completed | June 20, 2026, 11:31 p.m. |
Created at: May 1, 2026, 2:01 a.m.