Triple
T33087350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erwig |
E846675
|
entity |
| Predicate | territoryIncluded |
P11790
|
FINISHED |
| Object |
Septimania in southern Gaul
Septimania in southern Gaul was a late antique and early medieval region on the Mediterranean coast of what is now southern France, historically contested between Visigothic, Frankish, and later Muslim powers.
|
E2043403
|
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: Septimania in southern Gaul | Statement: [Erwig, territoryIncluded, Septimania in southern Gaul]
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: Septimania in southern Gaul Triple: [Erwig, territoryIncluded, Septimania in southern Gaul]
Generated description
Septimania in southern Gaul was a late antique and early medieval region on the Mediterranean coast of what is now southern France, historically contested between Visigothic, Frankish, and later Muslim powers.
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_69f34954d46c8190a04a159cc5f99efd |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d621c52c8190b7441de67c3af35c |
completed | May 3, 2026, 4:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3538f9b7748190a474cdc31e65fa3a |
completed | June 19, 2026, 12:41 p.m. |
| NEDg | Description generation | batch_6a353a52990c8190ac75c74034e8cc39 |
completed | June 19, 2026, 12:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a353aa9c5848190b740bcee9a763cff |
completed | June 19, 2026, 12:48 p.m. |
Created at: May 1, 2026, 1:26 a.m.