Triple
T4221587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cambridge life sciences ecosystem |
E94351
|
entity |
| Predicate | hasKeyCompany |
P51457
|
FINISHED |
| Object | GSK |
E2086
|
NE 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: GSK | Statement: [Cambridge life sciences ecosystem, hasKeyCompany, GSK]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GSK Context triple: [Cambridge life sciences ecosystem, hasKeyCompany, GSK]
-
A.
GlaxoSmithKline
chosen
GlaxoSmithKline is a global biopharmaceutical company known for developing and manufacturing prescription medicines, vaccines, and consumer healthcare products.
-
B.
AstraZeneca
AstraZeneca is a global biopharmaceutical company known for researching, developing, and manufacturing prescription medicines across areas such as oncology, cardiovascular, respiratory, and immunology.
-
C.
Novartis
Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
-
D.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
E.
Pfizer
Pfizer is a major American multinational pharmaceutical and biotechnology corporation known for developing a wide range of prescription medicines and vaccines, including one of the first widely used COVID-19 vaccines.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69b3451997e08190851db4a9a588837d |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b34f7d20b48190a404638c68c31026 |
completed | March 12, 2026, 11:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5a858a5b08190990a896265ac9725 |
completed | March 14, 2026, 6:26 p.m. |
Created at: March 12, 2026, 11:04 p.m.