Triple
T17721033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern District of Montserrat |
E442336
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Sweeney’s
Sweeney’s is a settlement in the Northern District of Montserrat in the Caribbean island nation of Montserrat.
|
E1284552
|
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: Sweeney’s | Statement: [Northern District of Montserrat, contains, Sweeney’s]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sweeney’s Context triple: [Northern District of Montserrat, contains, Sweeney’s]
-
A.
Sweeney
Sweeney is a surname of Irish and Scottish origin borne by various notable individuals in entertainment, sports, and public life.
-
B.
Sweeney's Men
Sweeney's Men were an influential Irish folk group from the 1960s known for helping pioneer the Irish folk revival with innovative arrangements and instrumentation.
-
C.
The Sweeney
The Sweeney is a 1970s British television crime drama series following the tough, unorthodox methods of London’s Flying Squad police unit.
-
D.
Greenacre
Greenacre is a residential suburb in south-western Sydney, New South Wales, known for its multicultural community and proximity to Bankstown.
-
E.
Sawyers
Sawyers is an English occupational surname derived from the trade of cutting timber, commonly used as a plural or variant form of Sawyer.
- 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: Sweeney’s Triple: [Northern District of Montserrat, contains, Sweeney’s]
Generated description
Sweeney’s is a settlement in the Northern District of Montserrat in the Caribbean island nation of Montserrat.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sweeney’s Target entity description: Sweeney’s is a settlement in the Northern District of Montserrat in the Caribbean island nation of Montserrat.
-
A.
Sweeney
Sweeney is a surname of Irish and Scottish origin borne by various notable individuals in entertainment, sports, and public life.
-
B.
Sweeney's Men
Sweeney's Men were an influential Irish folk group from the 1960s known for helping pioneer the Irish folk revival with innovative arrangements and instrumentation.
-
C.
The Sweeney
The Sweeney is a 1970s British television crime drama series following the tough, unorthodox methods of London’s Flying Squad police unit.
-
D.
Greenacre
Greenacre is a residential suburb in south-western Sydney, New South Wales, known for its multicultural community and proximity to Bankstown.
-
E.
Sawyers
Sawyers is an English occupational surname derived from the trade of cutting timber, commonly used as a plural or variant form of Sawyer.
- 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_69d8b9ec79688190b86bdcef85a7b3aa |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4748520a881908dc3446d33236ff7 |
completed | April 19, 2026, 6:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02302defe081909eaeb1f90d497561 |
completed | May 11, 2026, 7:38 p.m. |
| NEDg | Description generation | batch_6a0231835c748190b6908d6ddb5f25b4 |
completed | May 11, 2026, 7:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0232033f788190b0be2d3976ed57e4 |
completed | May 11, 2026, 7:46 p.m. |
Created at: April 10, 2026, 10:07 a.m.