Triple
T13640330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arellano |
E325955
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Marcos Arellano
Marcos Arellano is a person notable enough to be recognized as a prominent bearer of the surname Arellano.
|
E1126259
|
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: Marcos Arellano | Statement: [Arellano, hasNotableBearer, Marcos Arellano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marcos Arellano Context triple: [Arellano, hasNotableBearer, Marcos Arellano]
-
A.
Antonio Arellano
Antonio Arellano is a notable individual recognized for achievements that have made the surname Arellano prominent.
-
B.
Héctor Arellano
Héctor Arellano is a person notable enough to be recognized as a prominent bearer of the surname Arellano.
-
C.
Pablo Galindo
Pablo Galindo is a Python core developer and software engineer known for his work on the language’s internals, including co-authoring structural pattern matching (PEP 634) and contributing extensively to CPython.
-
D.
Miguel Arellano
Miguel Arellano is a person notable enough to be recognized as a prominent bearer of the surname Arellano.
-
E.
Carlos Ochoa
Carlos Ochoa is a personal name shared by multiple individuals, including professionals and public figures in various fields.
- 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: Marcos Arellano Triple: [Arellano, hasNotableBearer, Marcos Arellano]
Generated description
Marcos Arellano is a person notable enough to be recognized as a prominent bearer of the surname Arellano.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marcos Arellano Target entity description: Marcos Arellano is a person notable enough to be recognized as a prominent bearer of the surname Arellano.
-
A.
Antonio Arellano
Antonio Arellano is a notable individual recognized for achievements that have made the surname Arellano prominent.
-
B.
Héctor Arellano
Héctor Arellano is a person notable enough to be recognized as a prominent bearer of the surname Arellano.
-
C.
Pablo Galindo
Pablo Galindo is a Python core developer and software engineer known for his work on the language’s internals, including co-authoring structural pattern matching (PEP 634) and contributing extensively to CPython.
-
D.
Miguel Arellano
Miguel Arellano is a person notable enough to be recognized as a prominent bearer of the surname Arellano.
-
E.
Carlos Ochoa
Carlos Ochoa is a personal name shared by multiple individuals, including professionals and public figures in various fields.
- 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_69d8076beddc8190a53156f5bea77f5e |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc5aac7308190a3fd26baeade8f2a |
completed | April 12, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b366470819093a74828e2a85116 |
completed | May 8, 2026, 11:01 p.m. |
| NEDg | Description generation | batch_69fe6c2b7ec08190ba0b4a30cbb738e8 |
completed | May 8, 2026, 11:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe6fff4f408190ac51668da19db284 |
completed | May 8, 2026, 11:21 p.m. |
Created at: April 9, 2026, 9:51 p.m.