Triple
T5354169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kurt |
E102649
|
entity |
| Predicate | hasCognate |
P2525
|
FINISHED |
| Object | Conrad |
E228611
|
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: Conrad | Statement: [Kurt, hasCognate, Conrad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Conrad Context triple: [Kurt, hasCognate, Conrad]
-
A.
Conrad
Conrad is a character in Horace Walpole’s pioneering Gothic novel "The Castle of Otranto," whose fate helps set the story’s dark and supernatural events in motion.
-
B.
Conrad
chosen
Conrad is a masculine given name of Germanic origin, commonly used in various European countries and the English-speaking world.
-
C.
Conrad
Conrad is the daring pirate hero of Lord Byron’s narrative poem “Le Corsaire,” known for his rebellious spirit and tragic romantic fate.
-
D.
Kurtz
Kurtz is a surname of German origin borne by various notable individuals across fields such as philosophy, literature, and entertainment.
-
E.
Marlow
Marlow is a historic English town on the River Thames in Buckinghamshire, known for its picturesque setting, suspension bridge, and literary associations.
- 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_69bd43d8f7248190b64c140734b5c9a8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd86149774819084049a8785e3f2e0 |
completed | March 20, 2026, 5:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21dbc540819086aca16af1aa6213 |
completed | March 21, 2026, 10:55 p.m. |
Created at: March 20, 2026, 2:01 p.m.