Triple
T10845242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gracie Otto |
E255993
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Gracie Otto |
E255993
|
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: Gracie Otto | Statement: [Gracie Otto, name, Gracie Otto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gracie Otto Context triple: [Gracie Otto, name, Gracie Otto]
-
A.
Gracie Otto
chosen
Gracie Otto is an Australian filmmaker and actress known for her work in independent cinema and documentary directing.
-
B.
Gracie Kramer
Gracie Kramer is a former UCLA Bruins gymnast known for her powerful floor routines and leadership as a standout collegiate athlete.
-
C.
Gracie
Gracie is a given name, often used as a diminutive of Grace.
-
D.
Gracie Watson
Gracie Watson is a famous child whose elaborate grave monument and haunting legend make her one of the most iconic figures associated with Savannah, Georgia’s Bonaventure Cemetery.
-
E.
Gracie Hart
Gracie Hart is a tough yet endearingly awkward FBI agent and former undercover beauty pageant contestant known for blending sharp investigative skills with comedic charm.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d750d0155c81908fb55ba6b45db800 |
completed | April 9, 2026, 7:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dff7ca05788190aff4f34084499ba6 |
completed | April 15, 2026, 8:40 p.m. |
Created at: April 8, 2026, 9:19 p.m.