Triple
T12674034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alliance Healthcare |
E302757
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object |
Alloga
Alloga is a healthcare logistics and distribution company specializing in supply chain solutions for the pharmaceutical and healthcare industries.
|
E997484
|
NE FINISHED |
How this triple was built (4 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: Alloga | Statement: [Alliance Healthcare, hasBrand, Alloga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alloga Context triple: [Alliance Healthcare, hasBrand, Alloga]
-
A.
Gradoli
Gradoli is a small historic town in Italy’s Lazio region, situated in the hills above Lake Bolsena and known for its scenic views and Renaissance architecture.
-
B.
Alagna
Alagna is an Italian surname most prominently associated with French operatic tenor Roberto Alagna.
-
C.
Alberoni
Alberoni is an Italian surname most notably associated with Giulio Alberoni, an influential 18th-century cardinal and statesman.
-
D.
Almancil
Almancil is a town in Portugal’s Algarve region known for its luxury tourism, proximity to exclusive resorts like Quinta do Lago and Vale do Lobo, and traditional Portuguese architecture.
-
E.
Algieba
Algieba is a bright double star system in the constellation Leo, known for its striking golden components and prominence in amateur astronomy observations.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Alloga Triple: [Alliance Healthcare, hasBrand, Alloga]
Generated description
Alloga is a healthcare logistics and distribution company specializing in supply chain solutions for the pharmaceutical and healthcare industries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Alloga Target entity description: Alloga is a healthcare logistics and distribution company specializing in supply chain solutions for the pharmaceutical and healthcare industries.
-
A.
Gradoli
Gradoli is a small historic town in Italy’s Lazio region, situated in the hills above Lake Bolsena and known for its scenic views and Renaissance architecture.
-
B.
Alagna
Alagna is an Italian surname most prominently associated with French operatic tenor Roberto Alagna.
-
C.
Alberoni
Alberoni is an Italian surname most notably associated with Giulio Alberoni, an influential 18th-century cardinal and statesman.
-
D.
Almancil
Almancil is a town in Portugal’s Algarve region known for its luxury tourism, proximity to exclusive resorts like Quinta do Lago and Vale do Lobo, and traditional Portuguese architecture.
-
E.
Algieba
Algieba is a bright double star system in the constellation Leo, known for its striking golden components and prominence in amateur astronomy observations.
- F. None of above. chosen
Provenance (5 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961af991c8190b6079cb57e593b8f |
completed | April 10, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671a163188190b6077c77f9a81681 |
completed | May 2, 2026, 9:50 p.m. |
| NEDg | Description generation | batch_69f672d16f8881908c6d5cfed0b3ec3a |
completed | May 2, 2026, 9:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f67396fdac8190970068b2c39ad2f7 |
completed | May 2, 2026, 9:58 p.m. |
Created at: April 9, 2026, 5:20 p.m.