Triple
T14135026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Hassett |
E350268
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hassett |
E1047897
|
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: Hassett | Statement: [Joe Hassett, familyName, Hassett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hassett Context triple: [Joe Hassett, familyName, Hassett]
-
A.
Hassett
chosen
Hassett is a surname most notably associated with New Zealand footballer Betsy Hassett.
-
B.
Hassler
Hassler Whitney was an influential American mathematician known for his foundational work in differential topology and manifold theory.
-
C.
Heseltine
Heseltine is a surname most prominently associated with Michael Heseltine, a senior British Conservative politician and former Deputy Prime Minister.
-
D.
Hewett
Hewett is an English surname borne by various notable individuals across fields such as sports, arts, and public life.
-
E.
Hartnett
Hartnett is a surname most prominently associated with American actor Josh Hartnett, known for his film and television roles since the late 1990s.
- 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de610e949c8190852d336c9d12bfd0 |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcdf14439c81908b2a9999a35cc346 |
completed | May 7, 2026, 6:51 p.m. |
Created at: April 9, 2026, 11:57 p.m.