Triple
T30860142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neufchâteau |
E786036
|
entity |
| Predicate | locatedOnTransportRoute |
P2409
|
FINISHED |
| Object |
National road N85
National road N85 is a Belgian national highway that serves as a regional transport route connecting towns such as Neufchâteau in the Walloon region.
|
E1935924
|
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: National road N85 | Statement: [Neufchâteau, locatedOnTransportRoute, National road N85]
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: National road N85 Triple: [Neufchâteau, locatedOnTransportRoute, National road N85]
Generated description
National road N85 is a Belgian national highway that serves as a regional transport route connecting towns such as Neufchâteau in the Walloon region.
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_69f224b91c14819084e764832fe67a57 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f691a7353c8190bf1cf3ead5cc6421 |
completed | May 3, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a28c7d289a481909d435fda0e114f59 |
completed | June 10, 2026, 2:11 a.m. |
| NEDg | Description generation | batch_6a28cb7550f081908eb5a195718e55e6 |
completed | June 10, 2026, 2:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a28cc7003c081908373122f59284b68 |
completed | June 10, 2026, 2:31 a.m. |
Created at: April 29, 2026, 8:47 p.m.