Triple
T3407955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taylor Lautner |
E71820
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Taylor Lautner |
E71820
|
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: Taylor Lautner | Statement: [Taylor Lautner, name, Taylor Lautner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taylor Lautner Context triple: [Taylor Lautner, name, Taylor Lautner]
-
A.
Taylor Lautner
chosen
Taylor Lautner is an American actor best known for playing werewolf Jacob Black in the Twilight film series.
-
B.
Zac Efron
Zac Efron is an American actor and singer who rose to fame with Disney’s "High School Musical" series and has since starred in a variety of film and television roles.
-
C.
Josh Hutcherson
Josh Hutcherson is an American actor best known for his role as Peeta Mellark in "The Hunger Games" film series.
-
D.
Wes Bentley
Wes Bentley is an American actor known for his breakout role in "American Beauty" and appearances in films like "The Hunger Games" and "Interstellar."
-
E.
Logan Lerman
Logan Lerman is an American actor best known for his lead role in the "Percy Jackson" film series and performances in movies such as "The Perks of Being a Wallflower" and "Fury."
- 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_69ad85ac312481909e7027ced1456a9f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb9056acc8190a9c50ec374851ac8 |
completed | March 8, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b35468916c8190ba6caa2c53e01d9c |
completed | March 13, 2026, 12:03 a.m. |
Created at: March 8, 2026, 3:15 p.m.