Triple
T9634349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Communication and Concurrency |
E232890
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | LOTOS |
E807612
|
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: LOTOS | Statement: [Communication and Concurrency, influenced, LOTOS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LOTOS Context triple: [Communication and Concurrency, influenced, LOTOS]
-
A.
LOTOS
chosen
LOTOS is a formal specification language for describing and analyzing the behavior of distributed and concurrent systems, particularly in communication protocols.
-
B.
Lotso
Lotso is the strawberry-scented teddy bear who serves as the main antagonist in Pixar's animated film Toy Story 3.
-
C.
LOTG
LOTG is the standard abbreviation for the Laws of the Game, the official rulebook that governs how association football (soccer) is played worldwide.
-
D.
LOT
LOT is the national flag carrier airline of Poland, headquartered in Warsaw and operating an extensive network of domestic and international flights.
-
E.
LÖ
LÖ is the vehicle registration code for the district of Lörrach in the German state of Baden-Württemberg.
- 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_69ca848940cc8190b97cec654cb3bb4a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9b2a0e2c8190ab5aaa223b1e1cde |
completed | April 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18237e2608190a3e7d45231a35efd |
completed | April 4, 2026, 9:27 p.m. |
Created at: March 30, 2026, 8:11 p.m.