Triple
T12425911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Megan Fox |
E296897
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Megan Denise Fox |
E296897
|
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: Megan Denise Fox | Statement: [Megan Fox, fullName, Megan Denise Fox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Megan Denise Fox Context triple: [Megan Fox, fullName, Megan Denise Fox]
-
A.
Megan Fox
chosen
Megan Fox is an American actress and model known for her roles in blockbuster action films such as the Transformers series and various high-profile Hollywood productions.
-
B.
Isabel LaBeouf
Isabel LaBeouf is the daughter of American actor and filmmaker Shia LaBeouf.
-
C.
Sanaa Lathan
Sanaa Lathan is an American actress known for her work in film, television, and voice acting, including prominent roles in movies like "Love & Basketball" and "Brown Sugar."
-
D.
Hayden Panettiere
Hayden Panettiere is an American actress and singer best known for her roles in the TV series "Heroes" and "Nashville," as well as numerous film and voice-acting performances.
-
E.
Meagan Good
Meagan Good is an American actress known for her work in film and television, particularly in romantic comedies and dramas.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d7ccda08190be2ff1739c1c6855 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f0265fc81909a6288d11b78c2f9 |
completed | May 2, 2026, 6:14 p.m. |
Created at: April 8, 2026, 9:55 p.m.