Triple
T7721422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laura Lopes |
E175020
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Laura |
E142585
|
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: Laura | Statement: [Laura Lopes, givenName, Laura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Context triple: [Laura Lopes, givenName, Laura]
-
A.
Laura
Laura is a classic 1944 American film noir mystery celebrated for its sophisticated storytelling, atmospheric cinematography, and iconic score.
-
B.
Laura
chosen
Laura is a feminine given name of Latin origin, commonly used in many languages and cultures.
-
C.
Laura Jeanne
Laura Jeanne is the birth name of American actress and producer Reese Witherspoon, known for films like "Legally Blonde" and "Walk the Line."
-
D.
Lisa
Lisa is the given name of Australian musician and composer Lisa Gerrard, renowned for her work as part of Dead Can Dance and for her film scores.
-
E.
Lisa
Lisa is the central protagonist of the film "Wicker Park," around whom the story’s romantic mystery and emotional tension revolve.
- 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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702f0366c8190a78f0b03f090fc2c |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b517c64881908d24e8613dc33bf4 |
completed | March 29, 2026, 5:13 a.m. |
Created at: March 27, 2026, 4:05 p.m.