Triple
T16848339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuno of Rheinfelden |
E409602
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kuno |
E784896
|
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: Kuno | Statement: [Kuno of Rheinfelden, givenName, Kuno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kuno Context triple: [Kuno of Rheinfelden, givenName, Kuno]
-
A.
Kuno
Kuno is the rebellious central character in E.M. Forster’s dystopian science fiction story "The Machine Stops," who challenges the oppressive, technology-dependent society in which he lives.
-
B.
Kuno
chosen
Kuno is a masculine given name of German origin, historically borne by various nobles and notable figures in German-speaking regions.
-
C.
Okapa
Okapa is a rural town in Papua New Guinea known for its highland culture and production of premium coffee.
-
D.
Komo
The Komo are an ethnic group indigenous to western Ethiopia, particularly associated with the Gambela Region, with their own distinct language and cultural traditions.
-
E.
Komo
Komo is a town located in Hela Province in the Highlands region of Papua New Guinea.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b376bac48190ae09f29a28c55f8c |
completed | April 18, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bb1d555c8190883c82e562b7bfe9 |
completed | May 10, 2026, 5:06 p.m. |
Created at: April 10, 2026, 5:24 a.m.