Triple
T9852202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christian Hansen |
E239494
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hansen |
E263797
|
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: Hansen | Statement: [Christian Hansen, familyName, Hansen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hansen Context triple: [Christian Hansen, familyName, Hansen]
-
A.
Hansen
chosen
Hansen is a common Scandinavian-origin surname borne by numerous notable individuals across fields such as sports, politics, science, and the arts.
-
B.
Hahn
Hahn is a surname of German origin borne by various notable individuals across fields such as science, sports, and the arts.
-
C.
Hansson
Hansson is a common Swedish surname borne by numerous notable figures in politics, sports, and the arts.
-
D.
Hassler
Hassler Whitney was an influential American mathematician known for his foundational work in differential topology and manifold theory.
-
E.
Hans Hansen
Hans Hansen is a central character in Thomas Mann's novella "Tonio Kröger," representing the idealized, conventional bourgeois youth who contrasts with the artistic, introspective protagonist.
- 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb375ba448190a32cca2b0f376ac1 |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5ee190c8190957451d8d8291df3 |
completed | April 5, 2026, 3:24 a.m. |
Created at: March 30, 2026, 8:34 p.m.