Triple
T10491763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haggard |
E247436
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Mila Haggard
Mila Haggard is a person notable for bearing the surname Haggard, which is associated with several prominent figures in arts and entertainment.
|
E868801
|
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: Mila Haggard | Statement: [Haggard, hasNotableBearer, Mila Haggard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mila Haggard Context triple: [Haggard, hasNotableBearer, Mila Haggard]
-
A.
Milynn Sarley
Milynn Sarley is an American actress and internet personality known for her roles in low-budget fantasy and action films as well as her presence in online geek and gaming communities.
-
B.
Laura Harrington
Laura Harrington is an American actress best known for her role in the 1986 Stephen King film "Maximum Overdrive."
-
C.
Madelyn Salosihn
Madelyn Salosihn was the first wife of American comic actor Oliver Hardy, known for his work in the Laurel and Hardy comedy duo.
-
D.
Margo Gregg
Margo Gregg is known as the wife of legendary Northern Irish football goalkeeper Harry Gregg.
-
E.
Lea Hurst
Lea Hurst is a historic country house in Derbyshire, England, best known as the childhood home of Florence Nightingale.
- 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: Mila Haggard Triple: [Haggard, hasNotableBearer, Mila Haggard]
Generated description
Mila Haggard is a person notable for bearing the surname Haggard, which is associated with several prominent figures in arts and entertainment.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mila Haggard Target entity description: Mila Haggard is a person notable for bearing the surname Haggard, which is associated with several prominent figures in arts and entertainment.
-
A.
Milynn Sarley
Milynn Sarley is an American actress and internet personality known for her roles in low-budget fantasy and action films as well as her presence in online geek and gaming communities.
-
B.
Laura Harrington
Laura Harrington is an American actress best known for her role in the 1986 Stephen King film "Maximum Overdrive."
-
C.
Madelyn Salosihn
Madelyn Salosihn was the first wife of American comic actor Oliver Hardy, known for his work in the Laurel and Hardy comedy duo.
-
D.
Margo Gregg
Margo Gregg is known as the wife of legendary Northern Irish football goalkeeper Harry Gregg.
-
E.
Lea Hurst
Lea Hurst is a historic country house in Derbyshire, England, best known as the childhood home of Florence Nightingale.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5097e1c888190bc8e039f2e46181e |
completed | April 7, 2026, 1:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90dd040f48190a645ebd131f9205c |
completed | April 10, 2026, 2:48 p.m. |
| NEDg | Description generation | batch_69d911dd4198819089585462af6b5ef5 |
completed | April 10, 2026, 3:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d9126254408190a79ea571649416f6 |
completed | April 10, 2026, 3:08 p.m. |
Created at: April 6, 2026, 12:24 p.m.