Triple
T17265649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lauren Chapin |
E419119
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lauren |
E478716
|
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: Lauren | Statement: [Lauren Chapin, givenName, Lauren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauren Context triple: [Lauren Chapin, givenName, Lauren]
-
A.
Lauren
chosen
Lauren is a common given name used for people of any gender in various English-speaking and other countries.
-
B.
Lauren
Lauren is a fictional character known for performing the song "The History of Wrong Guys," typically portrayed as a humorous, self-aware romantic lead in musical theatre.
-
C.
Lauren
Lauren is a central female protagonist in the romantic comedy film "Think Like a Man," portrayed as a successful, relationship-seeking woman whose love life is influenced by Steve Harvey’s dating advice.
-
D.
Lauren
Lauren is a central character in the musical "Kinky Boots," known as a quirky, down-to-earth factory worker who becomes a key ally and love interest to the protagonist.
-
E.
Lauren
Lauren is a character featured in the song "Take What You Got."
- 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42f44ec7c81909a925fc8692b0a6c |
completed | April 19, 2026, 1:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01794641648190a5db87ecb359c17a |
completed | May 11, 2026, 6:37 a.m. |
Created at: April 10, 2026, 5:40 a.m.