Triple
T1723453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugo |
E37442
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Hauke |
E112274
|
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: Hauke | Statement: [Hugo, hasVariant, Hauke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hauke Context triple: [Hugo, hasVariant, Hauke]
-
A.
Hauke
chosen
Hauke is a Germanic given name, particularly common in Northern Germany, that is cognate with the English name Hugh.
-
B.
Helleren
Helleren is a small historic settlement in Norway known for its traditional houses built under a large rock overhang near the Jøssingfjord.
-
C.
Löhr
Löhr is a German-language surname borne by various notable individuals, including figures in military, arts, and public life.
-
D.
Heidkrüger
Heidkrüger is the original German surname of actress and former fashion model Diane Kruger.
-
E.
Günther
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
- 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_69a8861acab88190bb43cde203429399 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa63587ce08190a6a9e6dae11a708c |
completed | March 6, 2026, 5:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0d58cd08190bdda9f03ca458081 |
completed | March 8, 2026, 4:16 p.m. |
Created at: March 4, 2026, 7:30 p.m.