Triple
T11979763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tierney |
E285126
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
James Tierney
James Tierney is a name shared by several notable individuals, including figures in law, politics, and academia.
|
E968951
|
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: James Tierney | Statement: [Tierney, hasNotableBearer, James Tierney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: James Tierney Context triple: [Tierney, hasNotableBearer, James Tierney]
-
A.
Bill Tierney
Bill Tierney is a Hall of Fame American lacrosse coach renowned for leading multiple NCAA championship teams, most notably at Princeton and later at the University of Denver.
-
B.
Paul Tierney
Paul Tierney is an English professional football referee who officiates in the Premier League.
-
C.
Ray Tierney
Ray Tierney is the morally conflicted NYPD detective at the center of the crime drama film "Pride and Glory," who is forced to confront corruption within his own family and police department.
-
D.
Tom Tierney
Tom Tierney is an Irish rugby union coach and former player who notably served as head coach of the Ireland women's national team.
-
E.
Stephen Tierney
Stephen Tierney is a constitutional scholar known for his work on constitutional theory, federalism, and the law of referendums.
- 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: James Tierney Triple: [Tierney, hasNotableBearer, James Tierney]
Generated description
James Tierney is a name shared by several notable individuals, including figures in law, politics, and academia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: James Tierney Target entity description: James Tierney is a name shared by several notable individuals, including figures in law, politics, and academia.
-
A.
Bill Tierney
Bill Tierney is a Hall of Fame American lacrosse coach renowned for leading multiple NCAA championship teams, most notably at Princeton and later at the University of Denver.
-
B.
Paul Tierney
Paul Tierney is an English professional football referee who officiates in the Premier League.
-
C.
Ray Tierney
Ray Tierney is the morally conflicted NYPD detective at the center of the crime drama film "Pride and Glory," who is forced to confront corruption within his own family and police department.
-
D.
Tom Tierney
Tom Tierney is an Irish rugby union coach and former player who notably served as head coach of the Ireland women's national team.
-
E.
Stephen Tierney
Stephen Tierney is a constitutional scholar known for his work on constitutional theory, federalism, and the law of referendums.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90393cfb08190b5b45d3e5e32fad3 |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a5b024c81909e4ccfd7dec7edb3 |
completed | May 2, 2026, 2:29 p.m. |
| NEDg | Description generation | batch_69f60bda16e48190af8abc0aa8ef41f0 |
completed | May 2, 2026, 2:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f60cd1668881908f43d895fcfba0aa |
completed | May 2, 2026, 2:40 p.m. |
Created at: April 8, 2026, 9:46 p.m.