Triple
T29265653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Orleans restaurants |
E741969
|
entity |
| Predicate | haveNotableArea |
P131006
|
FINISHED |
| Object | French Quarter |
—
|
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: French Quarter | Statement: [New Orleans restaurants, haveNotableArea, French Quarter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveNotableArea Context triple: [New Orleans restaurants, haveNotableArea, French Quarter]
-
A.
hasNotableCollectionArea
Indicates that an entity possesses a significant or distinguished collection focused on a particular subject, theme, or domain.
-
B.
hasNotabilityRegion
chosen
Indicates that an entity’s notability, prominence, or recognition is specifically associated with a particular geographic region.
-
C.
notableAreaWithin
Indicates that one area is a particularly significant or noteworthy part located inside another, larger area.
-
D.
hasSubstantiveArea
Indicates that one entity is associated with, or falls within, a particular substantive field or domain of activity, knowledge, or regulation.
-
E.
hasNotableStructureOn
Indicates that a subject entity possesses or features a significant or noteworthy structure located on it.
- 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_69f0912065c08190bddd23e20e8ef18e |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f664df4a58819080ba470aae922b71 |
completed | May 2, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69f660f2e3708190ab658652bcfc04d0 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 28, 2026, 12:44 p.m.