Triple
T5127771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugene Ionesco |
E115624
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Eugen |
E305910
|
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: Eugen | Statement: [Eugene Ionesco, givenName, Eugen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eugen Context triple: [Eugene Ionesco, givenName, Eugen]
-
A.
Eugen
chosen
Eugen is a masculine given name of Greek origin, commonly used in various European languages and derived from a word meaning "well-born" or "noble."
-
B.
Eugen
Eugen is the given first name of the influential German playwright and poet Bertolt Brecht.
-
C.
Günther
Günther is the zoologist who first formally described the impressed tortoise species Manouria impressa.
-
D.
Günther
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
-
E.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
- 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_69bd444426bc819099ccd23f141e22aa |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd782428a081909583c7f368226daf |
completed | March 20, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bee06d63f8819081db05c0e5a06276 |
completed | March 21, 2026, 6:16 p.m. |
Created at: March 20, 2026, 1:42 p.m.