Triple
T496736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tobias Read |
E10309
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Read
Read is a surname shared by various notable individuals across fields such as politics, arts, and academia.
|
E61799
|
NE FINISHED |
How this triple was built (4 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: Read | Statement: [Tobias Read, familyName, Read]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Read Context triple: [Tobias Read, familyName, Read]
-
A.
Reading
Reading is a major town in Berkshire, England, known as a key commercial and transport hub in the Thames Valley.
-
B.
Woman Reading
Woman Reading is a painting by French artist Henri Matisse that exemplifies his use of bold color and simplified forms to depict an intimate, contemplative interior scene.
-
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.
RE
RE is the common abbreviation for the British Army’s Corps of Royal Engineers, responsible for military engineering and technical support.
-
E.
Reedus
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."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Read Triple: [Tobias Read, familyName, Read]
Generated description
Read is a surname shared by various notable individuals across fields such as politics, arts, and academia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Read Target entity description: Read is a surname shared by various notable individuals across fields such as politics, arts, and academia.
-
A.
Reading
Reading is a major town in Berkshire, England, known as a key commercial and transport hub in the Thames Valley.
-
B.
Woman Reading
Woman Reading is a painting by French artist Henri Matisse that exemplifies his use of bold color and simplified forms to depict an intimate, contemplative interior scene.
-
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.
RE
RE is the common abbreviation for the British Army’s Corps of Royal Engineers, responsible for military engineering and technical support.
-
E.
Reedus
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."
- F. None of above. chosen
Provenance (5 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f116f1b4819082f88d6c747368ae |
completed | Feb. 28, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a47d2f3a148190b1d81f0836063171 |
completed | March 1, 2026, 5:53 p.m. |
| NEDg | Description generation | batch_69a47eb3d90c81908e633300c0c8011b |
completed | March 1, 2026, 6 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a47f71e9408190b82f277bb74c2a13 |
completed | March 1, 2026, 6:03 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.