Triple
T1415929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John F. Kennedy Plaza |
E31915
|
entity |
| Predicate | hasRegulationHistory |
P27526
|
FINISHED |
| Object | former skateboarding hotspot |
—
|
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: former skateboarding hotspot | Statement: [John F. Kennedy Plaza, hasRegulationHistory, former skateboarding hotspot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRegulationHistory Context triple: [John F. Kennedy Plaza, hasRegulationHistory, former skateboarding hotspot]
-
A.
hasPolicyHistory
Indicates that an entity is associated with a record or sequence of past policies that have applied to it over time.
-
B.
hasHistoricalEntity
Indicates a relationship where one entity includes, references, or is associated with another entity that existed or is defined in a past historical context.
-
C.
historicalRecordStatus
Indicates the status or condition of a record within a historical or archival context (e.g., active, archived, revised, or obsolete).
-
D.
hasFireHistory
Indicates that an entity has experienced one or more fire events in the past.
-
E.
hasInflationHistory
Indicates that an entity is associated with a recorded or known pattern of inflation over time.
- 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_69a49919a994819086528951bc224775 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c402b1648190b87802d9beb2712e |
completed | March 1, 2026, 10:56 p.m. |
| PD | Predicate disambiguation | batch_69a4bf060b0081909ba00e6ac093a28b |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c06721488190ac7f6e012f21af3d |
completed | March 1, 2026, 10:40 p.m. |
Created at: March 1, 2026, 7:59 p.m.