Triple
T4481920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line A (Mexico City Metro) |
E100153
|
entity |
| Predicate | connectsCentralAreaWith |
P51738
|
FINISHED |
| Object | eastern suburbs of Mexico City |
—
|
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: eastern suburbs of Mexico City | Statement: [Line A (Mexico City Metro), connectsCentralAreaWith, eastern suburbs of Mexico City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsCentralAreaWith Context triple: [Line A (Mexico City Metro), connectsCentralAreaWith, eastern suburbs of Mexico City]
-
A.
connectsArea
Indicates that one area serves as a link or passage between two other areas, enabling movement or interaction between them.
-
B.
connectsViaCanal
Indicates that one entity is linked or joined to another by means of a canal.
-
C.
connectsUnder
Indicates that one entity forms a connection to another entity by passing beneath or under it.
-
D.
connectsTo
Indicates a relationship where one entity is linked or joined to another, allowing interaction, communication, or transfer between them.
-
E.
connectsLocation
chosen
Indicates a relationship where one entity serves as a link or route that joins or provides access between two locations.
- 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_69b34553cbe48190afa8ac1cac285b86 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35728ed508190ba0e882fa62d8848 |
completed | March 13, 2026, 12:15 a.m. |
| PD | Predicate disambiguation | batch_69b3563d63008190816e37027e761375 |
completed | March 13, 2026, 12:11 a.m. |
Created at: March 12, 2026, 11:36 p.m.