Triple
T8617484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Lautner |
E204077
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lautner |
E355308
|
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: Lautner | Statement: [John Lautner, familyName, Lautner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lautner Context triple: [John Lautner, familyName, Lautner]
-
A.
Lautner
chosen
Lautner is a surname most prominently associated with American actor Taylor Lautner, known for his role as Jacob Black in the "Twilight" film series.
-
B.
Laudner
Laudner is a surname most notably associated with former American Major League Baseball catcher Tim Laudner.
-
C.
Daniel Lautner
Daniel Lautner is the father of American actor Taylor Lautner, known for managing and supporting his son's entertainment career.
-
D.
Cundey
Cundey is a surname most notably associated with American cinematographer Dean Cundey, known for his work on influential genre and blockbuster films.
-
E.
Dellner
Dellner is a company specializing in railway coupling and connection systems used on modern passenger and freight trains worldwide.
- 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_69ca832ceab8819096e4a9f546695079 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc4711c7748190af26ff5a78ef66a2 |
completed | March 31, 2026, 10:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea923ae148190a973ef8ad6ccac9a |
completed | April 2, 2026, 5:36 p.m. |
Created at: March 30, 2026, 6:26 p.m.