Triple
T19445040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaffney, South Carolina |
E486452
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Michael Gaffney
Michael Gaffney was an early settler and prominent figure in South Carolina history for whom the city of Gaffney is named.
|
E1432680
|
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: Michael Gaffney | Statement: [Gaffney, South Carolina, namedAfter, Michael Gaffney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Gaffney Context triple: [Gaffney, South Carolina, namedAfter, Michael Gaffney]
-
A.
Andrew McDonough
Andrew McDonough is a voice actor known for his role in the animated film "Return to Never Land."
-
B.
Michael J. Shea
Michael J. Shea was an American composer best known for co-writing the iconic University of Notre Dame fight song, the "Notre Dame Victory March."
-
C.
Michael O’Rourke
Michael O’Rourke is best known as the father of the late child actress Heather O’Rourke, who starred in the "Poltergeist" film series.
-
D.
Michael O’Rourke
Michael O’Rourke is an Irish media entrepreneur best known as a co-founder of the international sports television network Setanta Sports.
-
E.
Michael K. Hannon
Michael K. Hannon is an American local politician who served as the mayor of Newark, California.
- 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: Michael Gaffney Triple: [Gaffney, South Carolina, namedAfter, Michael Gaffney]
Generated description
Michael Gaffney was an early settler and prominent figure in South Carolina history for whom the city of Gaffney is named.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Gaffney Target entity description: Michael Gaffney was an early settler and prominent figure in South Carolina history for whom the city of Gaffney is named.
-
A.
Andrew McDonough
Andrew McDonough is a voice actor known for his role in the animated film "Return to Never Land."
-
B.
Michael J. Shea
Michael J. Shea was an American composer best known for co-writing the iconic University of Notre Dame fight song, the "Notre Dame Victory March."
-
C.
Michael O’Rourke
Michael O’Rourke is an Irish media entrepreneur best known as a co-founder of the international sports television network Setanta Sports.
-
D.
Michael O’Rourke
Michael O’Rourke is best known as the father of the late child actress Heather O’Rourke, who starred in the "Poltergeist" film series.
-
E.
Michael K. Hannon
Michael K. Hannon is an American local politician who served as the mayor of Newark, California.
- 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6338921cc819083f8f918225d78e6 |
completed | April 20, 2026, 2:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a088af613dc81908d346f4df93f05fa |
completed | May 16, 2026, 3:19 p.m. |
| NEDg | Description generation | batch_6a088c0f55348190b33ab62ed033ed39 |
completed | May 16, 2026, 3:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a088d00188c8190bb8da973f31b1e47 |
completed | May 16, 2026, 3:28 p.m. |
Created at: April 10, 2026, 1:38 p.m.