Triple
T354281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vera Rubin |
E7509
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Vera
Vera Rubin was an influential American astronomer whose pioneering work on galaxy rotation curves provided key evidence for the existence of dark matter.
|
E58395
|
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: Vera | Statement: [Vera Rubin, givenName, Vera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vera Context triple: [Vera Rubin, givenName, Vera]
-
A.
Vera Boldis
Vera Boldis is best known as the former wife of Dee Dee Ramone, the bassist and songwriter of the pioneering punk rock band the Ramones.
-
B.
Varvara
Varvara is the Slavic form of the female given name Barbara, commonly used in Russian and other Eastern European languages.
-
C.
Anastasia Shubskaya
Anastasia Shubskaya is a Russian model and film producer best known as the wife of NHL star Alex Ovechkin.
-
D.
Celia Lovsky
Celia Lovsky was an Austrian-American character actress known for her distinctive roles in mid-20th-century film and television, including a memorable appearance as T’Pau in the original Star Trek series.
-
E.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
- 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: Vera Triple: [Vera Rubin, givenName, Vera]
Generated description
Vera Rubin was an influential American astronomer whose pioneering work on galaxy rotation curves provided key evidence for the existence of dark matter.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vera Target entity description: Vera Rubin was an influential American astronomer whose pioneering work on galaxy rotation curves provided key evidence for the existence of dark matter.
-
A.
Vera Boldis
Vera Boldis is best known as the former wife of Dee Dee Ramone, the bassist and songwriter of the pioneering punk rock band the Ramones.
-
B.
Varvara
Varvara is the Slavic form of the female given name Barbara, commonly used in Russian and other Eastern European languages.
-
C.
Anastasia Shubskaya
Anastasia Shubskaya is a Russian model and film producer best known as the wife of NHL star Alex Ovechkin.
-
D.
Celia Lovsky
Celia Lovsky was an Austrian-American character actress known for her distinctive roles in mid-20th-century film and television, including a memorable appearance as T’Pau in the original Star Trek series.
-
E.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
- 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_69a2eb8312f4819084dc222e665fded3 |
completed | Feb. 28, 2026, 1:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a457fa35988190851216c84ad63232 |
completed | March 1, 2026, 3:15 p.m. |
| NEDg | Description generation | batch_69a4585741188190a496ce65520424b4 |
completed | March 1, 2026, 3:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a458bb5d988190a706512529efa024 |
completed | March 1, 2026, 3:18 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.