Triple
T15121579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yummy |
E361183
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Tiffany McKie
Tiffany McKie is a writer known for her work on the food-focused publication Yummy.
|
E1137733
|
NE FINISHED |
How this triple was built (4 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: Tiffany McKie | Statement: [Yummy, writer, Tiffany McKie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tiffany McKie Context triple: [Yummy, writer, Tiffany McKie]
-
A.
Tiffany Mack
Tiffany Mack is an American actress best known for her role in the television series "Hap and Leonard."
-
B.
Tiffany Mitchell
Tiffany Mitchell is a fictional character from the British soap opera "EastEnders," known for her dramatic storylines and tragic death in the late 1990s.
-
C.
Jasmine McGlade
Jasmine McGlade is an American filmmaker, producer, and former competitive fencer known for her work on independent films and her past marriage to director Damien Chazelle.
-
D.
Teaira McCowan
Teaira McCowan is an American professional basketball center in the WNBA known for her dominant interior presence, rebounding, and shot-blocking ability.
-
E.
Kylie MacDonald
Kylie MacDonald is a fictional New York City police detective who co-leads the elite NYPD Red task force in James Patterson’s crime thriller series.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tiffany McKie Triple: [Yummy, writer, Tiffany McKie]
Generated description
Tiffany McKie is a writer known for her work on the food-focused publication Yummy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tiffany McKie Target entity description: Tiffany McKie is a writer known for her work on the food-focused publication Yummy.
-
A.
Tiffany Mack
Tiffany Mack is an American actress best known for her role in the television series "Hap and Leonard."
-
B.
Tiffany Mitchell
Tiffany Mitchell is a fictional character from the British soap opera "EastEnders," known for her dramatic storylines and tragic death in the late 1990s.
-
C.
Jasmine McGlade
Jasmine McGlade is an American filmmaker, producer, and former competitive fencer known for her work on independent films and her past marriage to director Damien Chazelle.
-
D.
Teaira McCowan
Teaira McCowan is an American professional basketball center in the WNBA known for her dominant interior presence, rebounding, and shot-blocking ability.
-
E.
Kylie MacDonald
Kylie MacDonald is a fictional New York City police detective who co-leads the elite NYPD Red task force in James Patterson’s crime thriller series.
- F. None of above. chosen
Provenance (5 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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0059f69a881909929a037a0eef702 |
completed | April 15, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feb7f4abd08190b47c9daff2921919 |
completed | May 9, 2026, 4:28 a.m. |
| NEDg | Description generation | batch_69feb8bb774481908272929358817440 |
completed | May 9, 2026, 4:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69feb9384b4c81909fe80dec3abc1659 |
completed | May 9, 2026, 4:34 a.m. |
Created at: April 10, 2026, 3:06 a.m.