Triple
T1163435
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Water Tower Place |
E24543
|
entity |
| Predicate | hasNumberOfStores |
P8902
|
FINISHED |
| Object | over 70 |
—
|
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: over 70 | Statement: [Water Tower Place, hasNumberOfStores, over 70]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfStores Context triple: [Water Tower Place, hasNumberOfStores, over 70]
-
A.
numberOfStores
chosen
Indicates the total count of stores associated with a given entity or context.
-
B.
hasRetailBoutiquesIn
Indicates that an entity operates or maintains retail boutiques located within a specified place or region.
-
C.
hasNumberOfCompanies
Indicates the quantitative relationship specifying how many companies are associated with a given entity.
-
D.
hasTeamStore
Indicates that an entity operates, is associated with, or provides access to a specific team-related retail store.
-
E.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
- 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_69a494060e148190abb42f971242c197 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bcc9dc5081908e225a485186ab12 |
completed | March 1, 2026, 10:25 p.m. |
| PD | Predicate disambiguation | batch_69a4bb525b648190adcb7a29256d3c41 |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:45 p.m.