Triple
T15347444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deborah Lautner |
E366959
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Deborah Lautner |
E366959
|
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: Deborah Lautner | Statement: [Deborah Lautner, name, Deborah Lautner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deborah Lautner Context triple: [Deborah Lautner, name, Deborah Lautner]
-
A.
Deborah Lautner
chosen
Deborah Lautner is the mother of American actor Taylor Lautner, known for his role as Jacob Black in the "Twilight" film series.
-
B.
Celeste Van Dien
Celeste Van Dien is the daughter of American actress Catherine Oxenberg and actor Casper Van Dien.
-
C.
Jennifer Leigh Morrow
Jennifer Leigh Morrow is the birth name of American actress, writer, and director Jennifer Jason Leigh, known for her intense and versatile performances in film and television.
-
D.
Gina Torres
Gina Torres is an American actress known for her roles in television series such as "Suits," "Firefly," and "Hannibal."
-
E.
Stephanie Zinone
Stephanie Zinone is the rebellious, motorcycle-loving leader of the Pink Ladies in the musical film "Grease 2."
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e1749bc8190a8b9cbcb27288a5b |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff364d82c48190b116528b5c00e918 |
completed | May 9, 2026, 1:27 p.m. |
Created at: April 10, 2026, 3:17 a.m.