Triple
T8063230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lauren Cohan |
E188175
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Lauren Cohan |
E188175
|
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: Lauren Cohan | Statement: [Lauren Cohan, birthName, Lauren Cohan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauren Cohan Context triple: [Lauren Cohan, birthName, Lauren Cohan]
-
A.
Lauren Cohan
chosen
Lauren Cohan is an American-British actress best known for playing Maggie Greene on the television series "The Walking Dead."
-
B.
Alexandra Reeve
Alexandra Reeve is the daughter of late actor and activist Christopher Reeve, known for her work in disability advocacy and public health.
-
C.
Samira Wiley
Samira Wiley is an American actress best known for her acclaimed role on the Netflix series "Orange Is the New Black" and her work on "The Handmaid's Tale."
-
D.
Nathalie Emmanuel
Nathalie Emmanuel is a British actress best known for her roles as Missandei in "Game of Thrones" and Ramsey in the "Fast & Furious" film franchise.
-
E.
Alex Kingston
Alex Kingston is an English actress best known for her roles in the television series "ER" and "Doctor Who," as well as numerous film and stage productions.
- 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_69ca82b42674819086840efea12478e5 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3fce95f08190b803956a20082e95 |
completed | March 31, 2026, 3:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc93d44c3481908b6e95ce8c78c602 |
completed | April 1, 2026, 3:41 a.m. |
Created at: March 30, 2026, 5:26 p.m.