Triple
T23063777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Antonio, Texas |
E574976
|
entity |
| Predicate | rankInPopulationInUS |
P471
|
FINISHED |
| Object | among the top 10 most populous U.S. cities |
—
|
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: among the top 10 most populous U.S. cities | Statement: [San Antonio, Texas, rankInPopulationInUS, among the top 10 most populous U.S. cities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInPopulationInUS Context triple: [San Antonio, Texas, rankInPopulationInUS, among the top 10 most populous U.S. cities]
-
A.
rankByPopulationInUnitedStates
Indicates the relative ordering of entities based on their population size within the United States.
-
B.
rankByPopulationInUS
chosen
Indicates the relative ordering of entities based on the size of their populations within the United States.
-
C.
areaRankInUS
Indicates the relative position of an entity in a ranking of areas within the United States, based on its size.
-
D.
frequencyRankInUnitedStates
Indicates the relative position of something in an ordered list based on how frequently it occurs within the United States.
-
E.
rankInUS2010Census
Indicates the numerical position of an entity in the ranking of occurrences within the 2010 United States Census.
- 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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f189a1f49c81909db7e0473ec2bb1b |
completed | April 29, 2026, 4:31 a.m. |
| PD | Predicate disambiguation | batch_69ef89d5f71881908b9f9d0c8aab278c |
completed | April 27, 2026, 4:07 p.m. |
Created at: April 17, 2026, 3:55 p.m.