Triple
T12991111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Empress |
E321906
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | Lai Lai |
E1013970
|
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: Lai Lai | Statement: [Empress, hasTrack, Lai Lai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lai Lai Context triple: [Empress, hasTrack, Lai Lai]
-
A.
Lai Lai
chosen
Lai Lai is a character featured in the Filipino superhero television series "Woman of Steel."
-
B.
Feizi
Feizi was an early Chinese nobleman of the Zhou dynasty who established the lineage that would become the powerful State of Qin and later unify China under the Qin dynasty.
-
C.
Lou-lan
Lou-lan is an ancient Silk Road kingdom and archaeological site located near the former shoreline of Lop Nur in what is now Xinjiang, China.
-
D.
Luli
Luli is a dialect of the Paama language, spoken by a subset of Paama speakers in Vanuatu.
-
E.
Linzi
Linzi was the prominent ancient Chinese city that served as the political, economic, and cultural center of the powerful State of Qi during the Zhou dynasty.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e7765788190a9503ef055bc30ca |
completed | April 10, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c0fca5e4819086b010fdd1813419 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:43 p.m.