Triple
T2610903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1000 Fifth Avenue, New York, NY |
E58768
|
entity |
| Predicate | hasStateCode |
P15056
|
FINISHED |
| Object | NY |
—
|
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: NY | Statement: [1000 Fifth Avenue, New York, NY, hasStateCode, NY]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStateCode Context triple: [1000 Fifth Avenue, New York, NY, hasStateCode, NY]
-
A.
hasParticipantStateOrProvince
Indicates that an entity’s participation in an event, activity, or relationship is associated with a specific state or province as its geographic context.
-
B.
hasAddressState
chosen
Indicates that an entity’s address is located within a particular state or state-level administrative region.
-
C.
hasRegionCode
Indicates that an entity is associated with a specific regional identifier or code.
-
D.
hasCountyCode
Indicates that an entity is associated with a specific county identified by a standardized county code.
-
E.
hasPostalAbbreviationState
Indicates that a state is associated with a specific standardized postal abbreviation.
- 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_69ab4ac3523881909679750c9f8c2dec |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd89325308190985598373eb0d296 |
completed | March 7, 2026, 7:49 a.m. |
| PD | Predicate disambiguation | batch_69abd80cd7fc81909e9696db2919129f |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:50 p.m.