Triple
T17289178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roussillon plain |
E419737
|
entity |
| Predicate | hasRiver |
P165
|
FINISHED |
| Object | Tech |
E85348
|
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: Tech | Statement: [Roussillon plain, hasRiver, Tech]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tech Context triple: [Roussillon plain, hasRiver, Tech]
-
A.
Tech
Tech is a highly intelligent and tech-savvy clone commando in Star Wars: The Bad Batch, known for his analytical mind, technical expertise, and role as the squad’s systems and communications specialist.
-
B.
Tech
chosen
The Tech is a river in southern France that flows through the Pyrénées-Orientales in the Occitanie region before emptying into the Mediterranean Sea.
-
C.
Tekno
Tekno is a Nigerian singer, songwriter, and record producer known for his Afrobeat and Afropop hit songs and dance-oriented sound.
-
D.
Tech High
Tech High is a public high school in Oakland, California, known for its diverse student body, strong academic programs, and historic campus.
-
E.
r/technology
r/technology is a popular Reddit community dedicated to news, discussion, and analysis of current and emerging technologies and their impact on society.
- 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_69d886db32608190a61e18862c5a8af6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e43781b7808190a0528ee9c54a0c66 |
completed | April 19, 2026, 2:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a017957c738819087341bcd51b55114 |
completed | May 11, 2026, 6:38 a.m. |
Created at: April 10, 2026, 5:40 a.m.