Triple
T27861294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1111 Lincoln Road |
E704236
|
entity |
| Predicate | numberOfParkingLevels |
P6578
|
FINISHED |
| Object | multiple split-level decks |
—
|
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: multiple split-level decks | Statement: [1111 Lincoln Road, numberOfParkingLevels, multiple split-level decks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfParkingLevels Context triple: [1111 Lincoln Road, numberOfParkingLevels, multiple split-level decks]
-
A.
numberOfParkingSpaces
Indicates the total count of parking spaces associated with a particular entity or location.
-
B.
numberOfSeatingLevels
Indicates the total count of distinct seating levels or tiers associated with a venue, structure, or seating arrangement.
-
C.
numberOfLevels
chosen
Indicates the total count of hierarchical layers, stages, or floors associated with an entity.
-
D.
parkingStructure
Indicates that one entity is a parking facility or structure associated with another entity (such as a building, location, or organization).
-
E.
typicalParkingDepth
Indicates the usual or standard depth measurement associated with a parking space or parking area.
- 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_69ef840f12408190b539d00d79658abf |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69ff234f32888190a1d800a3bda432eb |
completed | May 9, 2026, 12:06 p.m. |
| PD | Predicate disambiguation | batch_69ff228ae9a0819083f4b97c10b923f4 |
completed | May 9, 2026, 12:03 p.m. |
Created at: April 27, 2026, 6:18 p.m.