Triple
T2823441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niall Quinn |
E54862
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Quinn |
E54862
|
NE FINISHED |
How this triple was built (2 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: Quinn | Statement: [Niall Quinn, familyName, Quinn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Quinn Context triple: [Niall Quinn, familyName, Quinn]
-
A.
Quinn
chosen
Quinn is a surname of Irish origin commonly borne by individuals such as American football coach Dan Quinn.
-
B.
Noelle Quinn
Noelle Quinn is a former standout UCLA Bruins guard who became a WNBA player and later head coach of the Seattle Storm.
-
C.
Skylar
Skylar is a compassionate and intelligent Harvard student who becomes Will Hunting’s love interest in the film "Good Will Hunting."
-
D.
Riley
Riley is a surname most famously associated with Pat Riley, the legendary NBA coach and executive.
-
E.
Kendall
Kendall is a neighborhood in Cambridge, Massachusetts, known for its proximity to MIT and its concentration of technology companies and research institutions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde71fdc08190b18660261fe24adf |
completed | March 7, 2026, 8:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b055d3dbb8819094df5e6751dd96c4 |
completed | March 10, 2026, 5:33 p.m. |
Created at: March 6, 2026, 9:59 p.m.