Triple
T32510573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Target pharmacy and clinic businesses |
E830918
|
entity |
| Predicate | numberOfClinicsAtAcquisition |
P67173
|
FINISHED |
| Object | 80 |
—
|
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: 80 | Statement: [Target pharmacy and clinic businesses, numberOfClinicsAtAcquisition, 80]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfClinicsAtAcquisition Context triple: [Target pharmacy and clinic businesses, numberOfClinicsAtAcquisition, 80]
-
A.
hasNumberOfClinics
chosen
Indicates the quantity of clinics associated with or belonging to a given entity.
-
B.
numberOfHospitals
Indicates the total count of hospitals associated with a given entity or within a specified context.
-
C.
numberOfDrillingCenters
Indicates the quantity of drilling centers associated with or involved in a given entity or context.
-
D.
hasNumberOfAgencies
Indicates the quantity of agencies associated with or linked to a given entity.
-
E.
numberOfStores
Indicates the total count of stores associated with a given entity or context.
- 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_69f3492318348190ba37fb6b5f1d67f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a01c51e2d088190b9dcce5b6de1cb9b |
completed | May 11, 2026, 12:01 p.m. |
| PD | Predicate disambiguation | batch_6a01c4c715a48190b7435de8a9d86fd4 |
completed | May 11, 2026, noon |
Created at: May 1, 2026, 1 a.m.