Triple
T32691921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vera Wang |
E835880
|
entity |
| Predicate | openedFirstBridalSalonIn |
P39513
|
FINISHED |
| Object | New York City |
—
|
NE NERFINISHED |
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: New York City | Statement: [Vera Wang, openedFirstBridalSalonIn, New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: openedFirstBridalSalonIn Context triple: [Vera Wang, openedFirstBridalSalonIn, New York City]
-
A.
openedFirstBoutiqueIn
chosen
Indicates that an entity established its first boutique or retail store in a specified location.
-
B.
firstCeremony
Indicates that an entity participates in or is associated with the earliest or initial ceremony in a sequence or context.
-
C.
officialOpeningYear
Indicates the calendar year in which something was formally opened or inaugurated for official use.
-
D.
firstOpenedAt
Indicates the date and time at which something was initially opened for the first time.
-
E.
openedAt
Indicates that an entity began operating, became accessible, or was first made available at a specific time or date.
- 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_69f3493211388190993801216afbc2a7 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fcdf2394748190b35cead3e208447d |
completed | May 7, 2026, 6:51 p.m. |
| PD | Predicate disambiguation | batch_69fcdbe344ec8190a0471911952f4b82 |
completed | May 7, 2026, 6:37 p.m. |
Created at: May 1, 2026, 1:10 a.m.