Triple
T16953793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiësto |
E411244
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Annika Backes
Annika Backes is an American model known for her work in fashion and for being married to Dutch DJ and producer Tiësto.
|
E1243069
|
NE FINISHED |
How this triple was built (4 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: Annika Backes | Statement: [Tiësto, spouse, Annika Backes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Annika Backes Context triple: [Tiësto, spouse, Annika Backes]
-
A.
Annika Bruhn
Annika Bruhn is a German competitive swimmer who has represented Germany at multiple international championships, including the Olympic Games.
-
B.
Annika Hansen
Annika Hansen is the human birth name of Seven of Nine, a former Borg drone and prominent character in the Star Trek franchise.
-
C.
Annika Lammers
Annika Lammers is a person notable enough to be recognized as a bearer of the surname Lammers.
-
D.
Annika
Annika is a television crime drama series featuring Kate Dickie in a prominent role.
-
E.
Nadine Angerer
Nadine Angerer is a retired German goalkeeper widely regarded as one of the greatest in women's soccer history, known for her World Cup–winning performances and multiple international awards.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Annika Backes Triple: [Tiësto, spouse, Annika Backes]
Generated description
Annika Backes is an American model known for her work in fashion and for being married to Dutch DJ and producer Tiësto.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Annika Backes Target entity description: Annika Backes is an American model known for her work in fashion and for being married to Dutch DJ and producer Tiësto.
-
A.
Annika Bruhn
Annika Bruhn is a German competitive swimmer who has represented Germany at multiple international championships, including the Olympic Games.
-
B.
Annika Hansen
Annika Hansen is the human birth name of Seven of Nine, a former Borg drone and prominent character in the Star Trek franchise.
-
C.
Annika Lammers
Annika Lammers is a person notable enough to be recognized as a bearer of the surname Lammers.
-
D.
Annika
Annika is a television crime drama series featuring Kate Dickie in a prominent role.
-
E.
Nadine Angerer
Nadine Angerer is a retired German goalkeeper widely regarded as one of the greatest in women's soccer history, known for her World Cup–winning performances and multiple international awards.
- F. None of above. chosen
Provenance (5 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_69d886c9c9d481909afe222093641cae |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d01b04e88190a72735541b3bb117 |
completed | April 18, 2026, 6:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d464762c8190a734ffdd83633f70 |
completed | May 10, 2026, 6:54 p.m. |
| NEDg | Description generation | batch_6a00d53422408190ba91624194333c13 |
completed | May 10, 2026, 6:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00d5adee908190a13bfc765e7c8f06 |
completed | May 10, 2026, 6:59 p.m. |
Created at: April 10, 2026, 5:31 a.m.