Triple
T7272063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | dark forest hypothesis |
E161129
|
entity |
| Predicate | languageOfName |
P15
|
FINISHED |
| Object | English |
E211
|
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: English | Statement: [dark forest hypothesis, languageOfName, English]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: English Context triple: [dark forest hypothesis, languageOfName, English]
-
A.
English
chosen
English is a widely spoken West Germanic language that serves as a global lingua franca in education, business, science, and international communication.
-
B.
ENG
ENG is the three-letter FIFA country code used to represent the England national football team in international competitions and official records.
-
C.
EN
EN is the standard abbreviation used in Portugal for "Estrada Nacional," the national road network.
-
D.
Angolalla
Angolalla is a historic town in central Ethiopia known as the birthplace of Emperor Menelik II.
-
E.
World English
World English is a phonetic notation system developed by Alexander Melville Bell to represent the sounds of spoken English with precision.
- 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_69c6885181008190b419040e22939c7c |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb0a03888190b5aa1da80dd303c5 |
completed | March 27, 2026, 8:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7db25658481909fc8cf86deb436a4 |
completed | March 28, 2026, 1:44 p.m. |
Created at: March 27, 2026, 2:58 p.m.