Triple
T2965600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alison Brie |
E80154
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Alison |
E136320
|
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: Alison | Statement: [Alison Brie, givenName, Alison]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alison Context triple: [Alison Brie, givenName, Alison]
-
A.
Alison
chosen
Alison is a feminine given name of English origin, commonly used in many English-speaking countries.
-
B.
Annalise
Annalise is a minor but pivotal character in John le Carré’s espionage novel "Smiley’s People," involved in the intricate web of intelligence and personal relationships surrounding George Smiley.
-
C.
Laurie
Laurie is a charming, wealthy, and impulsive young man who becomes a close friend and would-be suitor to the March sisters in Louisa May Alcott’s novel "Little Women."
-
D.
Emma Chambers
Emma Chambers was an English actress best known for her comedic role as Alice Tinker in the television sitcom "The Vicar of Dibley."
-
E.
Alana
Alana is a feminine given name commonly used in English-speaking countries and various cultures worldwide.
- 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_69ad8b1341848190bd19dbf46892887d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad995a28e88190a4d6b9ef2c0d8e61 |
completed | March 8, 2026, 3:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b108e14e288190bcca59b2d8132996 |
completed | March 11, 2026, 6:17 a.m. |
Created at: March 8, 2026, 2:58 p.m.