Triple
T17318298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Socorro |
E420488
|
entity |
| Predicate | majorPopulation |
P126953
|
FINISHED |
| Object | humans |
—
|
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: humans | Statement: [Socorro, majorPopulation, humans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorPopulation Context triple: [Socorro, majorPopulation, humans]
-
A.
majorPopulationIn
Indicates that a significant portion of a population is located in or primarily associated with a particular place or region.
-
B.
majorPopulationCenter
Indicates that a location functions as a primary hub of population concentration and activity within a region.
-
C.
populationFocus
Indicates that something is primarily directed toward, concerned with, or designed for a particular population or demographic group.
-
D.
permanentPopulation
Indicates that an entity has a stable, long-term resident population rather than a temporary or transient presence.
-
E.
metropolitanAreaPopulationApproximate
Indicates that the predicate specifies an approximate total population size for a given metropolitan area.
- 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_69d889d22b848190a4663d0b8f8f76e7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4399ea7dc8190a0ecab7534fe16c3 |
completed | April 19, 2026, 2:10 a.m. |
| PD | Predicate disambiguation | batch_69e3b01b9d1c8190a406dd941c9b11a1 |
completed | April 18, 2026, 4:23 p.m. |
| PDg | Predicate description generation | batch_69e3b2a225b08190a50f984caa6513b9 |
completed | April 18, 2026, 4:34 p.m. |
Created at: April 10, 2026, 5:43 a.m.