Triple
T11319509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liev Schreiber |
E268055
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Schreiber |
E387616
|
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: Schreiber | Statement: [Liev Schreiber, familyName, Schreiber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schreiber Context triple: [Liev Schreiber, familyName, Schreiber]
-
A.
Schreiber
chosen
Schreiber is a surname most notably associated with Stuart L. Schreiber, a prominent American chemist known for his pioneering work in chemical biology and drug discovery.
-
B.
Schreiber
Schreiber is a small township and community located along the north shore of Lake Superior in northwestern Ontario, Canada.
-
C.
Witten
Witten is a surname most notably associated with Edward Witten, a leading theoretical physicist and key figure in string theory and mathematical physics.
-
D.
Witten
Witten is a city in the Ruhr region of western Germany known for its industrial heritage and location along the Ruhr River.
-
E.
Skriver
Skriver is a Danish surname most notably associated with fashion model Josephine Skriver.
- 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_69d6aaca5c24819083db46a30d86cb34 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9de875481908acfa56015d4b46f |
completed | April 9, 2026, 6:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e525e2549081909ec99e4c7006fd66 |
completed | April 19, 2026, 6:58 p.m. |
Created at: April 8, 2026, 9:32 p.m.