Triple
T14263879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camila Vallejo |
E353592
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Vallejo
Vallejo is a Spanish-origin surname borne by various notable individuals across politics, arts, and sports in Spanish-speaking countries.
|
E1089961
|
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: Vallejo | Statement: [Camila Vallejo, familyName, Vallejo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vallejo Context triple: [Camila Vallejo, familyName, Vallejo]
-
A.
Vallejo
Vallejo is a waterfront city in the San Francisco Bay Area known for its former Mare Island Naval Shipyard and diverse, working-class community.
-
B.
Vallejo
Vallejo is a metro station in Mexico City that serves passengers on Line 6 of the city’s rapid transit system.
-
C.
San Jose Diridon
San Jose Diridon is a major intermodal transit hub in San Jose, California, serving Amtrak, commuter rail, light rail, and bus services.
-
D.
Santa Cruz
Santa Cruz is a coastal municipality in the Philippine island province of Marinduque known for its fishing communities and rural island-barangays.
-
E.
Santa Cruz
Santa Cruz is a notable wine-producing city in central Chile’s Colchagua Valley, recognized for its vineyards, tourism, and colonial charm.
- 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: Vallejo Triple: [Camila Vallejo, familyName, Vallejo]
Generated description
Vallejo is a Spanish-origin surname borne by various notable individuals across politics, arts, and sports in Spanish-speaking countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vallejo Target entity description: Vallejo is a Spanish-origin surname borne by various notable individuals across politics, arts, and sports in Spanish-speaking countries.
-
A.
Vallejo
Vallejo is a waterfront city in the San Francisco Bay Area known for its former Mare Island Naval Shipyard and diverse, working-class community.
-
B.
Vallejo
Vallejo is a metro station in Mexico City that serves passengers on Line 6 of the city’s rapid transit system.
-
C.
San Jose Diridon
San Jose Diridon is a major intermodal transit hub in San Jose, California, serving Amtrak, commuter rail, light rail, and bus services.
-
D.
Santa Cruz
Santa Cruz is a coastal municipality in the Philippine island province of Marinduque known for its fishing communities and rural island-barangays.
-
E.
Santa Cruz
Santa Cruz is a notable wine-producing city in central Chile’s Colchagua Valley, recognized for its vineyards, tourism, and colonial charm.
- 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6357a8188190ba518a486521052b |
completed | April 14, 2026, 3:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd326367348190b4b31b32f4ca5639 |
completed | May 8, 2026, 12:46 a.m. |
| NEDg | Description generation | batch_69fd335a496881908689b5769bb677ae |
completed | May 8, 2026, 12:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd3445422c8190a22eaa4bef5fdf76 |
completed | May 8, 2026, 12:54 a.m. |
Created at: April 10, 2026, 1:09 a.m.