Triple
T12947795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louisiana Art & Science Museum |
E309813
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
LASM
LASM is a museum in Baton Rouge that combines art exhibitions, science displays, and a planetarium to provide interdisciplinary educational experiences.
|
E1010611
|
NE FINISHED |
How this triple was built (4 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: LASM | Statement: [Louisiana Art & Science Museum, shortName, LASM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LASM Context triple: [Louisiana Art & Science Museum, shortName, LASM]
-
A.
LASK
LASK is a professional Austrian football club based in Linz that competes in the Austrian Bundesliga.
-
B.
LASAN
LASAN is the public agency responsible for managing wastewater, solid waste, and environmental services for the City of Los Angeles.
-
C.
Lasi
Lasi is a regional dialect of the Sindhi language spoken primarily in parts of Balochistan and Sindh in Pakistan.
-
D.
St Laserian
St Laserian was a 7th-century Irish bishop and saint, traditionally regarded as the founder and first bishop of the monastery and diocese at Old Leighlin in County Carlow, Ireland.
-
E.
C-LAS
C-LAS is the official abbreviation used in the Netherlands for the Commander of the Royal Netherlands Army, the highest-ranking officer in the Dutch land forces.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: LASM Triple: [Louisiana Art & Science Museum, shortName, LASM]
Generated description
LASM is a museum in Baton Rouge that combines art exhibitions, science displays, and a planetarium to provide interdisciplinary educational experiences.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LASM Target entity description: LASM is a museum in Baton Rouge that combines art exhibitions, science displays, and a planetarium to provide interdisciplinary educational experiences.
-
A.
LASK
LASK is a professional Austrian football club based in Linz that competes in the Austrian Bundesliga.
-
B.
LASAN
LASAN is the public agency responsible for managing wastewater, solid waste, and environmental services for the City of Los Angeles.
-
C.
Lasi
Lasi is a regional dialect of the Sindhi language spoken primarily in parts of Balochistan and Sindh in Pakistan.
-
D.
St Laserian
St Laserian was a 7th-century Irish bishop and saint, traditionally regarded as the founder and first bishop of the monastery and diocese at Old Leighlin in County Carlow, Ireland.
-
E.
C-LAS
C-LAS is the official abbreviation used in the Netherlands for the Commander of the Royal Netherlands Army, the highest-ranking officer in the Dutch land forces.
- F. None of above. chosen
Provenance (5 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e1c67b8819094e5243267f93ce2 |
completed | April 10, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af75bc04819098d98c47fca48ac9 |
completed | May 3, 2026, 2:14 a.m. |
| NEDg | Description generation | batch_69f6b02e3b9881909387c1f70176a1bd |
completed | May 3, 2026, 2:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6b11ced30819090f67a0b1e1369aa |
completed | May 3, 2026, 2:21 a.m. |
Created at: April 9, 2026, 5:43 p.m.