Triple
T21329045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael P. Hurley |
E525846
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Hurley |
—
|
NE NERFINISHED |
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: Hurley | Statement: [Michael P. Hurley, hasSurname, Hurley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hurley Context triple: [Michael P. Hurley, hasSurname, Hurley]
-
A.
Hurley
chosen
Hurley is a common English-language surname borne by various notable individuals across fields such as technology, sports, and entertainment.
-
B.
Hurley
Hurley is a 2010 studio album by American rock band Weezer, noted for its cover featuring actor Jorge Garcia and its blend of alternative rock and pop-punk.
-
C.
Hurley
Hurley is a film featuring Patrick Wilson that showcases his work as an actor.
-
D.
Hurley Reyes
Hurley Reyes is a beloved main character from the television series "Lost," known for his humor, kindness, and complex backstory involving a lottery win and perceived bad luck.
-
E.
Domino Hurley
Domino Hurley is a slick, antagonistic travel agent and rival to protagonist Manny Calavera in the noir-inspired adventure game Grim Fandango.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b51b90788190a4dd823d962626da |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7ab4f95fc819087eb32dca7da689a |
completed | April 21, 2026, 4:52 p.m. |
Created at: April 16, 2026, 4:42 p.m.