Triple
T5535557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kristin Davis |
E145152
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kristin |
E437279
|
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: Kristin | Statement: [Kristin Davis, givenName, Kristin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristin Context triple: [Kristin Davis, givenName, Kristin]
-
A.
Kristin
Kristin is one of the official mascots of the 1994 Winter Olympics held in Lillehammer, Norway.
-
B.
Kristin
chosen
Kristin is the central female protagonist of Sigrid Undset’s historical novel trilogy "Kristin Lavransdatter," set in medieval Norway.
-
C.
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."
-
D.
Kristen
Kristen is the birth name of Kris Jenner, the American television personality and matriarch of the Kardashian–Jenner family.
-
E.
Kristen
Kristen is a feminine given name commonly used in English-speaking countries, often associated with notable figures in entertainment and public life.
- 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_69c008f9955881909bfa8348b56b4739 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01faed1d08190b6a57faedaf1b56a |
completed | March 22, 2026, 4:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c02810084c81909fde56d6ee4ad328 |
completed | March 22, 2026, 5:34 p.m. |
Created at: March 22, 2026, 3:34 p.m.