Triple
T21346186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Otto Leonhard Heubner |
E526343
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Otto |
—
|
NE NERFINISHED |
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: Otto | Statement: [Otto Leonhard Heubner, givenName, Otto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Otto Context triple: [Otto Leonhard Heubner, givenName, Otto]
-
A.
Otto
Otto is the central German soldier protagonist in the 1993 war film "Stalingrad," whose experiences depict the brutality and futility of the Eastern Front in World War II.
-
B.
Otto
chosen
Otto is a given name of Germanic origin commonly used across various European countries.
-
C.
Otto
Otto was a lesser-known medieval Polish prince from the Piast dynasty, notable mainly as a younger son of Duke Casimir I the Restorer.
-
D.
Otto
Otto is the title of one of the early nominative reports that were later incorporated into the official United States Reports, documenting decisions of the U.S. Supreme Court.
-
E.
Otto
Otto was a self-driving truck startup focused on autonomous freight technology, later integrated into Uber’s autonomous vehicle efforts.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b51cd5cc81909ac1187971e8a8ad |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69ee5ba7ce3c8190ba5ded980a9866f2 |
completed | April 26, 2026, 6:38 p.m. |
Created at: April 16, 2026, 4:55 p.m.