Triple
T14912026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woodward & Lothrop |
E371283
|
entity |
| Predicate | peakNumberOfStoresApprox |
P8902
|
FINISHED |
| Object | 30 |
—
|
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: 30 | Statement: [Woodward & Lothrop, peakNumberOfStoresApprox, 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakNumberOfStoresApprox Context triple: [Woodward & Lothrop, peakNumberOfStoresApprox, 30]
-
A.
numberOfStores
chosen
Indicates the total count of stores associated with a given entity or context.
-
B.
hasRetailStores
Indicates that an entity operates or possesses one or more physical retail store locations.
-
C.
numberOfRestaurantsAndRetail
Indicates the total count of entities that are either restaurants or retail establishments associated with a given subject.
-
D.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
-
E.
numberOfFloorsInAnchorStores
Indicates the relationship specifying how many floors are contained within each anchor store.
- 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded61d75008190b6f9a1a38137836f |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de9a52ba988190a26e268b4ea083ea |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:26 a.m.