Triple
T6884256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tully Marshall |
E158875
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Anna May
Anna May was the wife of American character actor Tully Marshall, known primarily in relation to his personal life.
|
E626918
|
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: Anna May | Statement: [Tully Marshall, spouse, Anna May]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna May Context triple: [Tully Marshall, spouse, Anna May]
-
A.
Mary Louise May
Mary Louise May was the wife of American character actor Ward Bond, known for his prolific roles in classic Hollywood films and the television series "Wagon Train."
-
B.
Mabel Beckman
Mabel Beckman was a philanthropist and benefactor whose support helped establish the Beckman Institute for Advanced Science and Technology.
-
C.
Mary Elizabeth Gaud
Mary Elizabeth Gaud is known as the wife of William Gaud, a prominent American lawyer and World Bank official.
-
D.
Mary Elizabeth Ellis
Mary Elizabeth Ellis is an American actress and comedian best known for her recurring role as The Waitress on the TV series "It's Always Sunny in Philadelphia."
-
E.
Eunice Olsen
Eunice Olsen is a Singaporean former Nominated Member of Parliament, actress, television host, and advocate for women's and children's rights.
- 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: Anna May Triple: [Tully Marshall, spouse, Anna May]
Generated description
Anna May was the wife of American character actor Tully Marshall, known primarily in relation to his personal life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna May Target entity description: Anna May was the wife of American character actor Tully Marshall, known primarily in relation to his personal life.
-
A.
Mary Louise May
Mary Louise May was the wife of American character actor Ward Bond, known for his prolific roles in classic Hollywood films and the television series "Wagon Train."
-
B.
Mabel Beckman
Mabel Beckman was a philanthropist and benefactor whose support helped establish the Beckman Institute for Advanced Science and Technology.
-
C.
Mary Elizabeth Gaud
Mary Elizabeth Gaud is known as the wife of William Gaud, a prominent American lawyer and World Bank official.
-
D.
Mary Elizabeth Ellis
Mary Elizabeth Ellis is an American actress and comedian best known for her recurring role as The Waitress on the TV series "It's Always Sunny in Philadelphia."
-
E.
Eunice Olsen
Eunice Olsen is a Singaporean former Nominated Member of Parliament, actress, television host, and advocate for women's and children's rights.
- 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_69c688342f6c8190ad7eea6ba262db99 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d90a2590819092ff253dd66ebe8b |
completed | March 27, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c748c79d2c819097462b4517dd76d7 |
completed | March 28, 2026, 3:19 a.m. |
| NEDg | Description generation | batch_69c749b9f4048190b7f8564f804e1231 |
completed | March 28, 2026, 3:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c74a8b5af88190a60782e247129d1a |
completed | March 28, 2026, 3:27 a.m. |
Created at: March 27, 2026, 2:23 p.m.