Triple
T1356000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rob Lowe |
E28989
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Rob Lowe |
E28989
|
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: Rob Lowe | Statement: [Rob Lowe, name, Rob Lowe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rob Lowe Context triple: [Rob Lowe, name, Rob Lowe]
-
A.
Rob Lowe
chosen
Rob Lowe is an American actor known for his roles in films like "St. Elmo's Fire" and TV series such as "Parks and Recreation" and "9-1-1: Lone Star."
-
B.
Dermot Mulroney
Dermot Mulroney is an American actor known for his versatile film and television roles, including prominent performances in romantic comedies and dramas since the late 1980s.
-
C.
Luke Goss
Luke Goss is an English actor and former drummer best known for his roles in genre films such as "Blade II" and "Hellboy II: The Golden Army."
-
D.
Michael Ealy
Michael Ealy is an American actor known for his roles in films like "Barbershop," "Think Like a Man," and "2 Fast 2 Furious," as well as various television series.
-
E.
Thomas Haden Church
Thomas Haden Church is an American actor known for roles in the TV series "Wings" and films such as "Sideways" and "Spider-Man 3."
- 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_69a498571d248190a0ac9eb02d97097f |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c28c8dd0819082f94c9e7c837c5f |
completed | March 1, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad293ab8508190ac321c898cd3df39 |
completed | March 8, 2026, 7:46 a.m. |
Created at: March 1, 2026, 7:56 p.m.