Triple
T3433329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kristen Stewart |
E72389
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kristen |
E335804
|
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: Kristen | Statement: [Kristen Stewart, givenName, Kristen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristen Context triple: [Kristen Stewart, givenName, Kristen]
-
A.
Kristen
Kristen is a central female character in the romantic comedy film "Think Like a Man," whose love life and personal growth are explored through the movie’s ensemble relationship dynamics.
-
B.
Kristen
Kristen is the birth name of Kris Jenner, the American television personality and matriarch of the Kardashian–Jenner family.
-
C.
Kristen
chosen
Kristen is a feminine given name commonly used in English-speaking countries, often associated with notable figures in entertainment and public life.
-
D.
Kirsten
Kirsten is the first name of Kirsten Gillibrand, a prominent American politician and U.S. Senator from New York.
-
E.
Kristin
Kristin is the given name of the acclaimed British-French actress Kristin Scott Thomas, known for her roles in films such as "The English Patient" and "Four Weddings and a Funeral."
- 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_69ad85ae14308190bcbc25cfa0246c0b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb9c1d9148190b873ba66d34d4f01 |
completed | March 8, 2026, 6:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3547d3f4c8190bc6811398bd8f080 |
completed | March 13, 2026, 12:04 a.m. |
Created at: March 8, 2026, 3:15 p.m.