Triple
T13297063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luke Wilson |
E316711
|
entity |
| Predicate | portrayed |
P1668
|
FINISHED |
| Object | Richie Tenenbaum |
E310271
|
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: Richie Tenenbaum | Statement: [Luke Wilson, portrayed, Richie Tenenbaum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Richie Tenenbaum Context triple: [Luke Wilson, portrayed, Richie Tenenbaum]
-
A.
Richie Tenenbaum
chosen
Richie Tenenbaum is a melancholic former tennis prodigy and one of the eccentric siblings at the center of Wes Anderson’s film "The Royal Tenenbaums."
-
B.
Kim Billick
Kim Billick is best known as the wife of former NFL head coach and Super Bowl champion Brian Billick.
-
C.
Royal Tenenbaum
Royal Tenenbaum is the flawed, estranged patriarch of the eccentric Tenenbaum family in Wes Anderson’s film "The Royal Tenenbaums."
-
D.
Butters Stotch
Butters Stotch is a naive, soft-spoken, and often unlucky child character from the animated television series "South Park."
-
E.
Logan Green
Logan Green is an American entrepreneur best known as the co-founder and former CEO of the ride-sharing company Lyft.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990a2f2708190a8f2aa7e7c0b92d2 |
completed | April 11, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7266832a881909403c4d3cbe1edec |
completed | May 3, 2026, 10:41 a.m. |
Created at: April 9, 2026, 9:28 p.m.