Triple
T10381316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gene Tierney |
E244646
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Laura |
E168229
|
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: [Gene Tierney, notableWork, Laura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Context triple: [Gene Tierney, notableWork, Laura]
-
A.
Laura
Laura is a feminine given name of Latin origin, commonly used in many languages and cultures.
-
B.
Laura
chosen
Laura is a classic 1944 American film noir mystery celebrated for its sophisticated storytelling, atmospheric cinematography, and iconic score.
-
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 a feminine given name commonly used in English-speaking countries, often as a shortened form of Elizabeth or Melissa.
-
E.
Lisa
Lisa is the central female protagonist of the film "The Other Man," around whom the story’s romantic and dramatic tensions 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9921fa48190a874aa9a9e385b97 |
completed | April 7, 2026, 11:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d79592512c8190b999191f16e3133c |
completed | April 9, 2026, 12:03 p.m. |
Created at: April 6, 2026, 12:03 p.m.