Triple
T31504949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balbuena |
E803791
|
entity |
| Predicate | servesCitySector |
P198169
|
FINISHED |
| Object | eastern 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 Mexico City | Statement: [Balbuena, servesCitySector, eastern Mexico City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesCitySector Context triple: [Balbuena, servesCitySector, eastern Mexico City]
-
A.
sectorServed
Indicates the industry or economic sector that an entity primarily serves or targets with its activities, products, or services.
-
B.
cityServedType
Indicates the type or category of city that is served by a given entity (such as a facility, service, or infrastructure).
-
C.
associatedCityServed
Indicates that there is a relationship where a service, facility, or entity is linked to and serves a particular city.
-
D.
servesCityIndirectly
Indicates that an entity provides services or benefits to a city in an indirect or intermediary manner, rather than through direct interaction.
-
E.
strategicAreaServed
Indicates that an entity provides services or exerts influence within a particular area considered strategically important.
- F. None of above. chosen
Provenance (4 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_69f348cae52081909fa8e5f697523ae3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69feced53a7c819098ec474fb7d514b0 |
completed | May 9, 2026, 6:06 a.m. |
| PD | Predicate disambiguation | batch_69fecd9cd5288190aac8b4e04a7ee78e |
completed | May 9, 2026, 6:01 a.m. |
| PDg | Predicate description generation | batch_69feced436a48190b761db7df2d6b6bd |
completed | May 9, 2026, 6:06 a.m. |
Created at: April 30, 2026, 9:46 p.m.