Triple
T16336451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DAICHI |
E396689
|
entity |
| Predicate | successor |
P78
|
FINISHED |
| Object | DAICHI-2 |
E396689
|
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: DAICHI-2 | Statement: [DAICHI, successor, DAICHI-2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DAICHI-2 Context triple: [DAICHI, successor, DAICHI-2]
-
A.
DAICHI
chosen
DAICHI is a Japanese Advanced Land Observing Satellite (ALOS) designed for high-resolution Earth observation to support cartography, disaster monitoring, and environmental research.
-
B.
Daiukku
Daiukku is an alternative name for Deioces, the legendary founder and first king of the Median Empire in ancient Iran.
-
C.
Daik
Daik is a historic town on Lingga Island in Indonesia that once served as the political and cultural center of the Riau-Lingga Sultanate.
-
D.
Ibuki-2
Ibuki-2 is a Japanese Earth observation satellite dedicated to monitoring greenhouse gases and contributing to climate change research.
-
E.
Shubunka
Shubunka is a ruthless small-time racketeer and the central antihero of the 1947 film noir "The Gangster."
- 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_69d87f26864c819088365ca381a003c2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2c4e3af7881908a3116c41ed69115 |
completed | April 17, 2026, 11:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0026193a3c81909c640426ab798c1c |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:07 a.m.