Triple
T8498636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isabel Lahiri |
E201159
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Europol |
E36588
|
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: Europol | Statement: [Isabel Lahiri, employer, Europol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Europol Context triple: [Isabel Lahiri, employer, Europol]
-
A.
Europol
chosen
Europol is the European Union’s law enforcement agency that supports member states in combating serious international crime and terrorism.
-
B.
Evra
Evra is a French former professional footballer best known for his successful career as a left-back with Manchester United and the French national team.
-
C.
Europaeum
Europaeum is a network of leading European universities dedicated to promoting academic collaboration, European studies, and cross-border dialogue in higher education.
-
D.
Europos
Europos was an ancient Macedonian town traditionally identified as the birthplace of the Seleucid Empire’s founder, Seleucus I Nicator.
-
E.
Europos
Europos is an ancient city historically known as Rayy (or Rey), located near modern-day Tehran in Iran and recognized as one of the oldest continuously inhabited settlements in the region.
- 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_69ca831ee390819095fae73400bbfafc |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe583aebc819090345684bfa1fca2 |
completed | March 31, 2026, 3:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf9ffe17e481908516d2f526d60684 |
completed | April 3, 2026, 11:09 a.m. |
Created at: March 30, 2026, 6:14 p.m.