Triple
T15796046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth South |
E382980
|
entity |
| Predicate | stateElectorate |
P11212
|
FINISHED |
| Object |
Elizabeth
Elizabeth is an electoral district in South Australia represented in the state's House of Assembly.
|
E389234
|
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: Elizabeth | Statement: [Elizabeth South, stateElectorate, Elizabeth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Context triple: [Elizabeth South, stateElectorate, Elizabeth]
-
A.
Elizabeth
Elizabeth is a key character in Nathaniel Hawthorne’s short story “The Minister’s Black Veil,” serving as Reverend Hooper’s fiancée whose reaction to his mysterious veil highlights themes of isolation and the fear of hidden sin.
-
B.
Elizabeth
Elizabeth is the full given name of Betsy McCaughey, an American politician, writer, and former lieutenant governor of New York.
-
C.
Elizabeth
Elizabeth is the given name of Princess Elizabeth of Yugoslavia, a Yugoslav royal and public figure.
-
D.
Elizabeth
Elizabeth was a German noblewoman who held the title of Landgravine of Hesse-Homburg.
-
E.
Elizabeth
Elizabeth is the given first name of American actress Bess Armstrong, known for her work in film and television since the late 1970s.
- 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: Elizabeth Triple: [Elizabeth South, stateElectorate, Elizabeth]
Generated description
Elizabeth is an electoral district in South Australia represented in the state's House of Assembly.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Target entity description: Elizabeth is an electoral district in South Australia represented in the state's House of Assembly.
-
A.
Elizabeth
chosen
Elizabeth is a suburban electorate in South Australia known for its working-class community and industrial heritage within the northern Adelaide region.
-
B.
Elizabeth
Elizabeth is a city in northeastern New Jersey that forms part of the greater New York metropolitan area.
-
C.
Elizabeth
Elizabeth is the full given name of American politician Lizzie Fletcher, a U.S. Representative from Texas.
-
D.
Elizabeth
Elizabeth is a small town in Colorado known for its rural character and proximity to the Denver metropolitan area.
-
E.
Elizabeth
Elizabeth is the full given name of American attorney and politician Liz Cheney, a prominent conservative figure and former U.S. Representative from Wyoming.
- F. None of above.
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_69d86da16e188190b89af699f1ed0bfe |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4dc887081909d682ae153f06d97 |
completed | April 16, 2026, 10:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff998981088190b9ce9d99c0481e21 |
completed | May 9, 2026, 8:31 p.m. |
| NEDg | Description generation | batch_69ff9a215e7c8190a2b40fb027a38317 |
completed | May 9, 2026, 8:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff9a77d6d88190817158c30c56d70c |
completed | May 9, 2026, 8:35 p.m. |
Created at: April 10, 2026, 4:48 a.m.