Triple
T14460443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ciudad Acuña |
E358568
|
entity |
| Predicate | borderCityWith |
P15361
|
FINISHED |
| Object | Del Rio |
E243692
|
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: Del Rio | Statement: [Ciudad Acuña, borderCityWith, Del Rio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Del Rio Context triple: [Ciudad Acuña, borderCityWith, Del Rio]
-
A.
Del Rio
chosen
Del Rio is a border city in southwestern Texas known for its proximity to the Rio Grande and Laughlin Air Force Base.
-
B.
De la Garza
De la Garza is the fictional Mexican family surname central to Laura Esquivel’s novel "Like Water for Chocolate," associated with the domineering matriarch Mama Elena and her daughters.
-
C.
Álamos
Álamos is a residential neighborhood in Mexico City’s Benito Juárez borough, known for its central location and urban character.
-
D.
Laredo
Laredo is a coastal town in northern Spain’s Cantabria region, known for its long sandy beaches and historic old quarter.
-
E.
Laredo
Laredo is a base-level trim of the Jeep Grand Cherokee SUV, offering essential features at a more affordable price point.
- 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_69d82794dfa081909b9134ad2e32244b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91abc1008190a19de4f8f0112c9d |
completed | April 14, 2026, 7:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6495660081908ab9db11939e74f7 |
completed | May 8, 2026, 4:20 a.m. |
Created at: April 10, 2026, 1:19 a.m.