Triple
T11151556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hansen |
E263797
|
entity |
| Predicate | derivedFromGivenName |
P17
|
FINISHED |
| Object | Hans |
E75878
|
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: Hans | Statement: [Hansen, derivedFromGivenName, Hans]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hans Context triple: [Hansen, derivedFromGivenName, Hans]
-
A.
Hans
chosen
Hans is a masculine given name of Germanic origin commonly used in Germanic and Scandinavian countries.
-
B.
Hansi
Hansi is a historic town in the Hisar district of Haryana, India, known for its ancient forts and archaeological significance.
-
C.
Helmut
Helmut is a masculine given name of German origin, historically common in German-speaking countries.
-
D.
Oskar
Oskar is a masculine given name of Germanic origin, commonly used in various European countries.
-
E.
Wolfgang
Wolfgang is the given name of Johann Wolfgang von Goethe, the renowned German writer, poet, and statesman.
- 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_69d6aa9ccddc8190868998c8b7beb060 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8719e74819095413abc6c79296c |
completed | April 9, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e46334125c81909e2807b2c7e573b9 |
completed | April 19, 2026, 5:08 a.m. |
Created at: April 8, 2026, 9:28 p.m.