Triple
T1407993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norman Reedus |
E31739
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Reedus |
E31739
|
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: Reedus | Statement: [Norman Reedus, familyName, Reedus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Reedus Context triple: [Norman Reedus, familyName, Reedus]
-
A.
Reedus
chosen
Reedus is the surname of American actor and model Norman Reedus, best known for his role as Daryl Dixon on the television series "The Walking Dead."
-
B.
Elde
The Elde is a river in northern Germany that flows through the state of Mecklenburg-Vorpommern and joins the Elbe, serving as an important regional waterway.
-
C.
La Lecture
La Lecture is an early 20th-century painting by Pablo Picasso that depicts a contemplative female figure and reflects his evolving style during his transition from Cubism toward a more classical, figurative approach.
-
D.
In School
In School is a non-fiction book by former NHL goaltender and Canadian politician Ken Dryden that examines the challenges and possibilities of the modern education system.
-
E.
Tullistes
Tullistes are the inhabitants of the French city of Tulle, located in the Corrèze department in central France.
- 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_69a49918e1f88190ba610f9dc8114578 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3bf7f0c8190aee96818de6ff4a5 |
completed | March 1, 2026, 10:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ace5770ea08190ac91b47a4ed5bf35 |
completed | March 8, 2026, 2:56 a.m. |
Created at: March 1, 2026, 7:59 p.m.