Triple
T8464450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chula Vista |
E200124
|
entity |
| Predicate | hasDemonym |
P191
|
FINISHED |
| Object |
Chula Vistan
A Chula Vistan is a resident or native of Chula Vista, a coastal city in Southern California near San Diego.
|
E737074
|
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: Chula Vistan | Statement: [Chula Vista, hasDemonym, Chula Vistan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chula Vistan Context triple: [Chula Vista, hasDemonym, Chula Vistan]
-
A.
Chula
Chula is Thailand’s oldest and one of its most prestigious universities, renowned for its academic excellence and central role in the country’s higher education system.
-
B.
Paso Icalma
Paso Icalma is a mountain pass and international border crossing in the Andes connecting southern Argentina and Chile.
-
C.
Mesa Grande
Mesa Grande is a significant prehistoric platform mound and archaeological site in present-day Mesa, Arizona, associated with the ancient Hohokam civilization.
-
D.
Atalaya
Atalaya is a small Peruvian river port town in the Amazon rainforest, serving as a regional hub for transport and trade.
-
E.
Tafoya
Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
- 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: Chula Vistan Triple: [Chula Vista, hasDemonym, Chula Vistan]
Generated description
A Chula Vistan is a resident or native of Chula Vista, a coastal city in Southern California near San Diego.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chula Vistan Target entity description: A Chula Vistan is a resident or native of Chula Vista, a coastal city in Southern California near San Diego.
-
A.
Chula
Chula is Thailand’s oldest and one of its most prestigious universities, renowned for its academic excellence and central role in the country’s higher education system.
-
B.
Paso Icalma
Paso Icalma is a mountain pass and international border crossing in the Andes connecting southern Argentina and Chile.
-
C.
Mesa Grande
Mesa Grande is a significant prehistoric platform mound and archaeological site in present-day Mesa, Arizona, associated with the ancient Hohokam civilization.
-
D.
Atalaya
Atalaya is a small Peruvian river port town in the Amazon rainforest, serving as a regional hub for transport and trade.
-
E.
Tafoya
Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
- 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_69ca83198c4c8190a337bf717d1813f5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe4d05b2881909bddf58df0ee1143 |
completed | March 31, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce39e0d7788190add03271c940e1ff |
completed | April 2, 2026, 9:41 a.m. |
| NEDg | Description generation | batch_69ce3ad9a3a88190929a6c6ae7ee58cf |
completed | April 2, 2026, 9:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce3cc54d888190ae86b787afc38f7b |
completed | April 2, 2026, 9:54 a.m. |
Created at: March 30, 2026, 6:11 p.m.