Triple
T15748563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MTS |
E381786
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Chula Vista |
E38835
|
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: Chula Vista | Statement: [MTS, cityServed, Chula Vista]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chula Vista Context triple: [MTS, cityServed, Chula Vista]
-
A.
Chula Vista
chosen
Chula Vista is a large coastal city in Southern California known for its diverse communities, rapid suburban growth, and proximity to both downtown San Diego and the U.S.–Mexico border.
-
B.
El Cajon
El Cajon is a suburban city in Southern California’s East County region, located just east of San Diego.
-
C.
Carlsbad
Carlsbad is a city in southeastern New Mexico known as the gateway to Carlsbad Caverns National Park.
-
D.
Carlsbad
Carlsbad is a coastal city in northern San Diego County, California, known for its beaches, family attractions like LEGOLAND California, and affluent residential communities.
-
E.
Escondido
Escondido is a city in northern San Diego County, California, known as one of the region’s older inland communities with a mix of suburban neighborhoods, agriculture, and historic downtown areas.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0502fd3608190b42e647b9c2b41a1 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff830b85408190b9ae4d6752524b99 |
completed | May 9, 2026, 6:55 p.m. |
Created at: April 10, 2026, 4:46 a.m.