Triple
T9090885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennifer Smith |
E217878
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object |
Jennifer Smith
Jennifer Smith is a person whose specific identity or notable characteristics are not determined from the given information.
|
E217878
|
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: Jennifer Smith | Statement: [Jennifer Smith, name, Jennifer Smith]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jennifer Smith Context triple: [Jennifer Smith, name, Jennifer Smith]
-
A.
Jennifer Smith
Jennifer Smith is a Bermudian politician who served as Premier and was the first woman to lead the government of Bermuda.
-
B.
Karen Smith
Karen Smith is a naive and dim-witted but sweet member of the high school clique "The Plastics" in the teen comedy film Mean Girls.
-
C.
Karen Smith
Karen Smith is a British television producer best known for creating the hit dance competition series "Strictly Come Dancing."
-
D.
Julianne Smith
Julianne Smith is an American diplomat and foreign policy expert who serves as the United States Ambassador to the North Atlantic Treaty Organization (NATO).
-
E.
Jennifer McDaniel
Jennifer McDaniel is an American makeup artist best known for her former marriage to professional wrestling icon Hulk Hogan.
- 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: Jennifer Smith Triple: [Jennifer Smith, name, Jennifer Smith]
Generated description
Jennifer Smith is a person whose specific identity or notable characteristics are not determined from the given information.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jennifer Smith Target entity description: Jennifer Smith is a person whose specific identity or notable characteristics are not determined from the given information.
-
A.
Jennifer Smith
chosen
Jennifer Smith is a Bermudian politician who served as Premier and was the first woman to lead the government of Bermuda.
-
B.
Karen Smith
Karen Smith is a naive and dim-witted but sweet member of the high school clique "The Plastics" in the teen comedy film Mean Girls.
-
C.
Karen Smith
Karen Smith is a British television producer best known for creating the hit dance competition series "Strictly Come Dancing."
-
D.
Julianne Smith
Julianne Smith is an American diplomat and foreign policy expert who serves as the United States Ambassador to the North Atlantic Treaty Organization (NATO).
-
E.
Jennifer McDaniel
Jennifer McDaniel is an American makeup artist best known for her former marriage to professional wrestling icon Hulk Hogan.
- F. None of above.
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_69ca83d8ab5881909d8fddae363b32b1 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc965b73848190af309cd7d2f14066 |
completed | April 1, 2026, 3:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc2ca7a081908f597e9a58920d6e |
completed | April 5, 2026, 2:42 a.m. |
| NEDg | Description generation | batch_69d1ccb043008190a3af47b234520891 |
completed | April 5, 2026, 2:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1cd04694881909ab19cb4c11fbd20 |
completed | April 5, 2026, 2:46 a.m. |
Created at: March 30, 2026, 7:14 p.m.