Triple
T16745749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanne-Lore Behrens |
E406946
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Hanne-Lore Behrens |
E406946
|
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: Hanne-Lore Behrens | Statement: [Hanne-Lore Behrens, hasName, Hanne-Lore Behrens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanne-Lore Behrens Context triple: [Hanne-Lore Behrens, hasName, Hanne-Lore Behrens]
-
A.
Hanne-Lore Behrens
chosen
Hanne-Lore Behrens is a person notable enough to be cited as a prominent bearer of the surname Behrens.
-
B.
Jutta Behrendt
Jutta Behrendt is a former East German rower who won multiple Olympic and world championship medals during the 1980s.
-
C.
Katia Behrens
Katia Behrens is a notable individual who bears the surname Behrens, recognized for her significance among people with that name.
-
D.
Gertrud Strube
Gertrud Strube was the wife of German pathologist and Nobel laureate Gerhard Domagk, known for his pioneering work in antibacterial chemotherapy.
-
E.
Marion Hildebrandt
Marion Hildebrandt is a fictional character from the television series "Crossroads."
- 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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3aa223aa88190a3c1805ece7317e2 |
completed | April 18, 2026, 3:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00aaf3c6088190a8301a9613d74474 |
completed | May 10, 2026, 3:57 p.m. |
Created at: April 10, 2026, 5:21 a.m.