Triple
T474386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Otay Mesa Port of Entry |
E9028
|
entity |
| Predicate | hasMexicanCounterpart |
P6587
|
FINISHED |
| Object | Mesa de Otay border facilities |
—
|
LITERAL 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: Mesa de Otay border facilities | Statement: [Otay Mesa Port of Entry, hasMexicanCounterpart, Mesa de Otay border facilities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMexicanCounterpart Context triple: [Otay Mesa Port of Entry, hasMexicanCounterpart, Mesa de Otay border facilities]
-
A.
hasCounterpart
chosen
Indicates that one entity corresponds to, matches, or serves as an equivalent or parallel version of another entity.
-
B.
hasNameInSpanish
Indicates that an entity is associated with a specific name expressed in the Spanish language.
-
C.
hasSignificantSpanishInfluence
Indicates that one entity has been strongly shaped or notably affected by Spanish culture, language, practices, or presence.
-
D.
isWalledCityNorthOfMexico
Indicates that a walled city is located geographically to the north of Mexico.
-
E.
officialNameInSpanish
Indicates the officially recognized name of an entity when expressed in the Spanish language.
- F. None of above.
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_69a2e7ff81708190b0507a24a997232c |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f039f3d88190a7c93ecbf1bf5f58 |
completed | Feb. 28, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69a2edeed31881908cf43beed410572d |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.