Triple
T2286388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tigray Region |
E51399
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Shire |
E252968
|
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: Shire | Statement: [Tigray Region, containsTown, Shire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shire Context triple: [Tigray Region, containsTown, Shire]
-
A.
Shire
chosen
Shire is a town in Ethiopia’s northern Tigray Region, known as a local administrative and commercial center and for its proximity to historic sites such as Axum.
-
B.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
C.
Lonza
Lonza is a global Swiss-based life sciences company specializing in pharmaceutical, biotech, and nutrition products and services, particularly in contract development and manufacturing.
-
D.
Novartis
Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
-
E.
AstraZeneca
AstraZeneca is a global biopharmaceutical company known for researching, developing, and manufacturing prescription medicines across areas such as oncology, cardiovascular, respiratory, and immunology.
- 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_69a88b09c644819090b503456d96bf70 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc24730208190af8a5cf443d334f7 |
completed | March 7, 2026, 6:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae894f9ff881909d1b3a7956d82576 |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:48 p.m.