Triple
T93566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shirley Ann Jackson |
E1880
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Shirley
Shirley is the given name of Shirley Ann Jackson, a prominent American physicist and trailblazing academic leader.
|
E7949
|
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: Shirley | Statement: [Shirley Ann Jackson, givenName, Shirley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shirley Context triple: [Shirley Ann Jackson, givenName, Shirley]
-
A.
Shirley
Shirley is a small town in north-central Massachusetts served by commuter rail on the MBTA Fitchburg Line.
-
B.
L’Enfant
L’Enfant is the surname of Pierre Charles L’Enfant, the French-born American architect and civil engineer best known for designing the basic plan for Washington, D.C.
-
C.
Rebecca
Rebecca is a feminine given name of Hebrew origin meaning “to tie” or “to bind,” widely used in English-speaking countries.
-
D.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
-
E.
Painted Ladies
Painted Ladies are a famous row of colorful Victorian and Edwardian houses in San Francisco, often photographed with the city skyline in the background.
- 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: Shirley Triple: [Shirley Ann Jackson, givenName, Shirley]
Generated description
Shirley is the given name of Shirley Ann Jackson, a prominent American physicist and trailblazing academic leader.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shirley Target entity description: Shirley is the given name of Shirley Ann Jackson, a prominent American physicist and trailblazing academic leader.
-
A.
Shirley
Shirley is a small town in north-central Massachusetts served by commuter rail on the MBTA Fitchburg Line.
-
B.
L’Enfant
L’Enfant is the surname of Pierre Charles L’Enfant, the French-born American architect and civil engineer best known for designing the basic plan for Washington, D.C.
-
C.
Rebecca
Rebecca is a feminine given name of Hebrew origin meaning “to tie” or “to bind,” widely used in English-speaking countries.
-
D.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
-
E.
Painted Ladies
Painted Ladies are a famous row of colorful Victorian and Edwardian houses in San Francisco, often photographed with the city skyline in the background.
- 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_69a24d1a97dc819094e6c021fe9b05a7 |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24fd28e988190bde699647ee5b16b |
completed | Feb. 28, 2026, 2:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a26245bf748190828d5cb4624b2a79 |
completed | Feb. 28, 2026, 3:34 a.m. |
| NEDg | Description generation | batch_69a262bec71481909b251923011ca502 |
completed | Feb. 28, 2026, 3:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a263626b7c8190b53469d93ac604d9 |
completed | Feb. 28, 2026, 3:39 a.m. |
Created at: Feb. 28, 2026, 2:07 a.m.