Triple
T20811568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michelle Trachtenberg |
E512318
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Trachtenberg |
—
|
NE NERFINISHED |
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: Trachtenberg | Statement: [Michelle Trachtenberg, familyName, Trachtenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trachtenberg Context triple: [Michelle Trachtenberg, familyName, Trachtenberg]
-
A.
Trachtenberg
chosen
Trachtenberg is a surname most notably associated with American filmmaker Dan Trachtenberg, known for directing genre films and television.
-
B.
Kalmus
Kalmus is a surname most notably associated with Herbert Kalmus, the co-founder of the pioneering color motion picture company Technicolor.
-
C.
Mereschkowski
Mereschkowski is the surname of Konstantin Mereschkowski, a Russian biologist known for proposing the theory of symbiogenesis in the early 20th century.
-
D.
Grosbard
Grosbard is a surname most notably associated with Ulu Grosbard, a Belgian-born American film and theater director.
-
E.
Weinitzen
Weinitzen is a municipality in the Austrian state of Styria, located near the city of Graz.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4cd25088190b48ca9700cd24efc |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2d338ac819096d4a33de831609e |
completed | April 21, 2026, 12:20 a.m. |
Created at: April 16, 2026, 12:40 p.m.