Triple
T23269447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DAMA/LIBRA |
E588247
|
entity |
| Predicate | successor |
P78
|
FINISHED |
| Object | DAMA/LIBRA-phase2 |
—
|
NE NERFINISHED |
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: DAMA/LIBRA-phase2 | Statement: [DAMA/LIBRA, successor, DAMA/LIBRA-phase2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DAMA/LIBRA-phase2 Context triple: [DAMA/LIBRA, successor, DAMA/LIBRA-phase2]
-
A.
DAMA/LIBRA
chosen
DAMA/LIBRA is a dark matter direct-detection experiment that uses highly radiopure sodium iodide scintillators to search for an annual modulation signal in underground measurements.
-
B.
DAM
DAM is the three-letter IATA airport code for Damascus International Airport, the main airport serving Syria’s capital city.
-
C.
DAM
DAM is a Frankfurt-based museum dedicated to the history, theory, and contemporary practice of architecture in Germany and beyond.
-
D.
DAM
DAM is the National Rail station code used to identify Dalmeny railway station in Scotland’s rail network.
-
E.
Lumada DataOps Suite
Lumada DataOps Suite is Hitachi Vantara’s integrated software platform for managing, orchestrating, and operationalizing data across hybrid and multi-cloud environments to support analytics and digital transformation.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e25d148adc819088efbf42672604e9 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1957219188190b30bceffad1542da |
completed | April 29, 2026, 5:21 a.m. |
Created at: April 17, 2026, 4:45 p.m.