Triple
T20005134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doozer |
E494434
|
entity |
| Predicate | associatedWithNetwork |
P2830
|
FINISHED |
| Object | ABC |
—
|
NE NERFINISHED |
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: ABC | Statement: [Doozer, associatedWithNetwork, ABC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ABC Context triple: [Doozer, associatedWithNetwork, ABC]
-
A.
ABC
ABC is the abbreviation commonly used for the Associated British Corporation, a former British television company.
-
B.
ABC
ABC is the commonly used abbreviation for the Brazilian Academy of Sciences, a leading scientific society that promotes research and scientific development in Brazil.
-
C.
ABC
ABC is the IATA airport code for Albacete Air Base, a military airfield located near Albacete in Spain.
-
D.
ABC
ABC is the stock ticker symbol for Abcam, a global life sciences company specializing in research antibodies, reagents, and related tools.
-
E.
ABC
chosen
ABC is a major American broadcast television network known for airing a wide range of national programming, including sports championships, news, and entertainment shows.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626b2d748190886981ea90c8b2ea |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e661a46c748190a141ab5aac6ea250 |
completed | April 20, 2026, 5:25 p.m. |
Created at: April 11, 2026, 3:33 p.m.