Triple
T4900907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wood |
E109794
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Natalie Wood |
E202322
|
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: Natalie Wood | Statement: [Wood, hasNotableBearer, Natalie Wood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Natalie Wood Context triple: [Wood, hasNotableBearer, Natalie Wood]
-
A.
Natalie Wood
chosen
Natalie Wood was a celebrated American film and television actress known for her roles in classics such as "Rebel Without a Cause," "West Side Story," and "Splendor in the Grass."
-
B.
Roz Kirby
Roz Kirby was the wife of legendary comic book artist Jack Kirby and a key supporter and manager of his career.
-
C.
Blue Mitchell
Blue Mitchell was an American jazz trumpeter known for his lyrical hard bop style and work with leading ensembles in the 1950s and 1960s.
-
D.
Lola Kirke
Lola Kirke is an English-American actress and musician best known for her breakout role in the film "Mistress America" and her work in the series "Mozart in the Jungle."
-
E.
Jean Seberg
Jean Seberg was an American actress and French New Wave icon best known for her role in Jean-Luc Godard’s film "Breathless."
- 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_69bd441180708190ba42ffb44fea533a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e4c9a788190aaceec00d0057143 |
completed | March 20, 2026, 3:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be6fd2a0348190a285ea1a62e7ae1b |
completed | March 21, 2026, 10:15 a.m. |
Created at: March 20, 2026, 1:28 p.m.