Triple
T8175832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Victory |
E190934
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | LVM |
E427648
|
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: LVM | Statement: [Victory, producer, LVM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LVM Context triple: [Victory, producer, LVM]
-
A.
LVM3
LVM3 is India’s heavy-lift launch vehicle developed by ISRO to carry large communication and deep-space satellites into orbit.
-
B.
Veritas Volume Manager (in some versions)
chosen
Veritas Volume Manager is a storage management software product that provides advanced logical volume management, including features like disk virtualization, mirroring, and dynamic resizing for enterprise environments.
-
C.
LVS
LVS is the stock ticker symbol for Las Vegas Sands Corp., a major global developer and operator of casino resorts and integrated entertainment properties.
-
D.
LFS
LFS (Log-structured File System) is a file system design that writes all data sequentially in a log-like structure to optimize write performance and crash recovery, and is implemented in NetBSD.
-
E.
LSM
LSM is an undergraduate dual-degree program at the University of Pennsylvania that integrates rigorous training in life sciences with business and management education.
- 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_69ca82c1c0a08190bf8692b4d91a03ca |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4ab8295081909a450fcaa34f6ec6 |
completed | March 31, 2026, 4:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbf6d4ba881908f1bac9cce6cc29d |
completed | April 1, 2026, 6:47 a.m. |
Created at: March 30, 2026, 5:40 p.m.