Triple
T1360671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southeastern Massachusetts |
E29090
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Sharon
Sharon is a suburban town in Norfolk County, Massachusetts, known for its residential character, natural conservation areas, and proximity to Boston.
|
E155753
|
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: Sharon | Statement: [Southeastern Massachusetts, containsTown, Sharon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sharon Context triple: [Southeastern Massachusetts, containsTown, Sharon]
-
A.
Sharon Black
Sharon Black is a notable individual whose achievements or public presence have made the surname Black recognizable in her context.
-
B.
Sharon Curry
Sharon Curry is the wife of Michael Bruce Curry, the Presiding Bishop of the Episcopal Church in the United States.
-
C.
Sharon Meadow
Sharon Meadow is a popular open grassy area in San Francisco’s Golden Gate Park often used for picnics, festivals, and outdoor events.
-
D.
Ronna
Ronna is a residential district within Södertälje Municipality in Sweden, known for its diverse population and suburban character.
-
E.
Marla Maples
Marla Maples is an American actress and television personality best known for her high-profile marriage to businessman and future U.S. President Donald Trump in the 1990s.
- 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: Sharon Triple: [Southeastern Massachusetts, containsTown, Sharon]
Generated description
Sharon is a suburban town in Norfolk County, Massachusetts, known for its residential character, natural conservation areas, and proximity to Boston.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sharon Target entity description: Sharon is a suburban town in Norfolk County, Massachusetts, known for its residential character, natural conservation areas, and proximity to Boston.
-
A.
Sharon Black
Sharon Black is a notable individual whose achievements or public presence have made the surname Black recognizable in her context.
-
B.
Sharon Curry
Sharon Curry is the wife of Michael Bruce Curry, the Presiding Bishop of the Episcopal Church in the United States.
-
C.
Sharon Meadow
Sharon Meadow is a popular open grassy area in San Francisco’s Golden Gate Park often used for picnics, festivals, and outdoor events.
-
D.
Ronna
Ronna is a residential district within Södertälje Municipality in Sweden, known for its diverse population and suburban character.
-
E.
Marla Maples
Marla Maples is an American actress and television personality best known for her high-profile marriage to businessman and future U.S. President Donald Trump in the 1990s.
- 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_69a498d77abc8190913bf57e5f51d2c4 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c2b156b081909c99ada70a969fc0 |
completed | March 1, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acce725fec819085f6de8e6e368aa4 |
completed | March 8, 2026, 1:18 a.m. |
| NEDg | Description generation | batch_69accf9ac120819084c21fb7b88c050a |
completed | March 8, 2026, 1:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69accffa9a40819083a3e55a5d83e040 |
completed | March 8, 2026, 1:25 a.m. |
Created at: March 1, 2026, 7:56 p.m.