Triple
T351108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elsa Einstein |
E7444
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Elsa
Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
|
E44923
|
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: Elsa | Statement: [Elsa Einstein, givenName, Elsa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elsa Context triple: [Elsa Einstein, givenName, Elsa]
-
A.
Anna
Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
-
B.
Princess May
Princess May, better known as Mary of Teck, was Queen consort of the United Kingdom as the wife of King George V and the mother of Kings Edward VIII and George VI.
-
C.
Alicia
Alicia is the given name of the American singer, songwriter, and pianist Alicia Keys, known for her soulful R&B music and powerful vocals.
-
D.
Hilda
Hilda is the middle name of Margaret Thatcher, the former Prime Minister of the United Kingdom.
-
E.
Emma
Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
- 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: Elsa Triple: [Elsa Einstein, givenName, Elsa]
Generated description
Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elsa Target entity description: Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
-
A.
Anna
Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
-
B.
Princess May
Princess May, better known as Mary of Teck, was Queen consort of the United Kingdom as the wife of King George V and the mother of Kings Edward VIII and George VI.
-
C.
Alicia
Alicia is the given name of the American singer, songwriter, and pianist Alicia Keys, known for her soulful R&B music and powerful vocals.
-
D.
Hilda
Hilda is the middle name of Margaret Thatcher, the former Prime Minister of the United Kingdom.
-
E.
Emma
Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
- 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_69a2e7e696948190bebc966535995e45 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eb7df63c8190b7cd1bcfdfd96187 |
completed | Feb. 28, 2026, 1:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3dd33a50c8190846d19c24eb74039 |
completed | March 1, 2026, 6:31 a.m. |
| NEDg | Description generation | batch_69a3ddca8d3c8190b5baafe999a26b58 |
completed | March 1, 2026, 6:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3de7f9b28819099cfe7c55a8168f0 |
completed | March 1, 2026, 6:36 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.