Triple
T2655633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Compton |
E54602
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Ann Compton
Ann Compton is an American journalist best known for her long tenure as a White House correspondent for ABC News.
|
E321366
|
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: Ann Compton | Statement: [Compton, hasNotableBearer, Ann Compton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ann Compton Context triple: [Compton, hasNotableBearer, Ann Compton]
-
A.
Catherine Compton
Catherine Compton was a British aristocrat and the mother of Spencer Perceval, the only British prime minister to have been assassinated.
-
B.
Ann Hearn
Ann Hearn is an American actress known for her supporting roles in film and television, including an appearance in the legal drama "The Accused."
-
C.
Fay Compton
Fay Compton was a prominent English stage and film actress of the early to mid-20th century, renowned for her Shakespearean roles and appearances in classic British cinema.
-
D.
Charlotte Coleman
Charlotte Coleman was a British actress best known for her role as Scarlett in the romantic comedy film "Four Weddings and a Funeral."
-
E.
Anna Nolin
Anna Nolin is an American educator and school district leader who serves as superintendent of the Newton Public Schools in Massachusetts.
- 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: Ann Compton Triple: [Compton, hasNotableBearer, Ann Compton]
Generated description
Ann Compton is an American journalist best known for her long tenure as a White House correspondent for ABC News.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ann Compton Target entity description: Ann Compton is an American journalist best known for her long tenure as a White House correspondent for ABC News.
-
A.
Catherine Compton
Catherine Compton was a British aristocrat and the mother of Spencer Perceval, the only British prime minister to have been assassinated.
-
B.
Ann Hearn
Ann Hearn is an American actress known for her supporting roles in film and television, including an appearance in the legal drama "The Accused."
-
C.
Fay Compton
Fay Compton was a prominent English stage and film actress of the early to mid-20th century, renowned for her Shakespearean roles and appearances in classic British cinema.
-
D.
Charlotte Coleman
Charlotte Coleman was a British actress best known for her role as Scarlett in the romantic comedy film "Four Weddings and a Funeral."
-
E.
Anna Nolin
Anna Nolin is an American educator and school district leader who serves as superintendent of the Newton Public Schools in Massachusetts.
- 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_69ab49e028948190b97e01d73548b1d9 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd933ec008190aef1442460c4cfbc |
completed | March 7, 2026, 7:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1de7ea6b48190a992ef2c4b3d7c42 |
completed | March 11, 2026, 9:28 p.m. |
| NEDg | Description generation | batch_69b1e4e2ccb08190932caf81646db1fe |
completed | March 11, 2026, 9:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1e536247c8190a2b3b0fc2d1095e5 |
completed | March 11, 2026, 9:57 p.m. |
Created at: March 6, 2026, 9:53 p.m.