Triple
T3792785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bridgeport |
E89696
|
entity |
| Predicate | hasDiverseUrbanPopulation |
P38709
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Bridgeport, hasDiverseUrbanPopulation, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDiverseUrbanPopulation Context triple: [Bridgeport, hasDiverseUrbanPopulation, true]
-
A.
hasEthnicallyMixedPopulation
Indicates that a population is composed of people from multiple distinct ethnic groups rather than being ethnically homogeneous.
-
B.
isMulticulturalCity
chosen
Indicates that a city is characterized by the presence and interaction of multiple cultural, ethnic, or linguistic communities.
-
C.
hasSignificantPopulationGroup
Indicates that an entity contains or is associated with a notable or substantial subgroup of a population, distinguished by shared characteristics or attributes.
-
D.
hasUrbanRuralMix
Indicates that something exhibits a combination or blend of both urban and rural characteristics or components.
-
E.
hasDiverseStudentBody
Indicates that an educational institution’s student population includes a wide range of backgrounds, characteristics, or identities.
- 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_69aed9597d6881909b6ee3b9de859223 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecefa3608190a7a20ed6df6a64b2 |
completed | March 9, 2026, 3:53 p.m. |
| PD | Predicate disambiguation | batch_69aee743c8d08190a9f9c97b836bd703 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:15 p.m.