Triple
T2865204
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colin Hanks |
E63421
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Roswell |
E50843
|
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: Roswell | Statement: [Colin Hanks, notableWork, Roswell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roswell Context triple: [Colin Hanks, notableWork, Roswell]
-
A.
Roswell
chosen
Roswell is a city in southeastern New Mexico best known for its association with the 1947 UFO incident and its strong aerospace and military presence.
-
B.
Rosewell
Rosewell is a small village in Midlothian, Scotland, known historically for its coal mining heritage and rural character.
-
C.
Fallon
Fallon is a small city in western Nevada known for its agricultural community, nearby Naval Air Station, and role as a regional service hub.
-
D.
Newcomb
Newcomb is a surname most notably associated with Simon Newcomb, a 19th-century Canadian-American astronomer and mathematician.
-
E.
Tully
Tully is a 2018 comedy-drama film starring Charlize Theron as an overwhelmed mother who forms an unexpected bond with her night nanny.
- 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_69ab4c42fb8c8190b36e161d47c03b81 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdfb9e64c819087b1a47caeb174d5 |
completed | March 7, 2026, 8:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0314baa408190b1b398dcfaa29c53 |
completed | March 10, 2026, 2:57 p.m. |
Created at: March 6, 2026, 10:02 p.m.