Triple
T7497726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ned Dorsey |
E177175
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Stacey Colbert |
E727183
|
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: Stacey Colbert | Statement: [Ned Dorsey, spouse, Stacey Colbert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stacey Colbert Context triple: [Ned Dorsey, spouse, Stacey Colbert]
-
A.
Stacey Colbert
chosen
Stacey Colbert is a central character in the 1990s sitcom "Ned and Stacey," portrayed as an ambitious, sharp-tongued journalist who enters a marriage of convenience with ad executive Ned Dorsey.
-
B.
Stacey Sutton
Stacey Sutton is a fictional geologist and Bond girl who appears as a key ally to James Bond in the 1985 film "A View to a Kill."
-
C.
Stacey Shipman
Stacey Shipman is a central character in the British sitcom "Gavin & Stacey," known for her sweet, bubbly personality and long-distance romance with Gavin Shipman.
-
D.
Stacy Barrett
Stacy Barrett is a bubbly, loyal, and somewhat ditzy best friend character from the legal comedy-drama TV series "Drop Dead Diva."
-
E.
Michelle Stacy
Michelle Stacy is an American former child voice actress best known for her roles in animated films of the 1970s and early 1980s.
- 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_69c69f2696688190915a8458f2398211 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5963d98819098275b161848d2d4 |
completed | March 27, 2026, 9:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce019fb0cc81908a24b0e39110325d |
completed | April 2, 2026, 5:41 a.m. |
Created at: March 27, 2026, 3:44 p.m.