Triple
T16403372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Merkert Chemistry Center |
E398357
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Merkert
Merkert is the namesake of the Merkert Chemistry Center, likely a notable figure associated with the field of chemistry or the institution that houses the center.
|
E1211690
|
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: Merkert | Statement: [Merkert Chemistry Center, namedAfter, Merkert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Merkert Context triple: [Merkert Chemistry Center, namedAfter, Merkert]
-
A.
Merkerson
Merkerson is the surname of S. Epatha Merkerson, an acclaimed American actress best known for her long-running role as Lieutenant Anita Van Buren on the television series "Law & Order."
-
B.
Merkys
Merkys is a river in Lithuania and Belarus that serves as one of the principal tributaries of the Neman (Niemen) River.
-
C.
Nortrup
Nortrup is a small municipality in Lower Saxony, Germany, situated within the Artland region.
-
D.
Merkens
Merkens is a German surname most notably associated with Olympic track cyclist Toni Merkens.
-
E.
Lemery
Lemery is a coastal municipality in the province of Batangas in the Philippines, known for its commercial activity and proximity to Taal Lake and Volcano.
- 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: Merkert Triple: [Merkert Chemistry Center, namedAfter, Merkert]
Generated description
Merkert is the namesake of the Merkert Chemistry Center, likely a notable figure associated with the field of chemistry or the institution that houses the center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Merkert Target entity description: Merkert is the namesake of the Merkert Chemistry Center, likely a notable figure associated with the field of chemistry or the institution that houses the center.
-
A.
Merkerson
Merkerson is the surname of S. Epatha Merkerson, an acclaimed American actress best known for her long-running role as Lieutenant Anita Van Buren on the television series "Law & Order."
-
B.
Merkys
Merkys is a river in Lithuania and Belarus that serves as one of the principal tributaries of the Neman (Niemen) River.
-
C.
Nortrup
Nortrup is a small municipality in Lower Saxony, Germany, situated within the Artland region.
-
D.
Merkens
Merkens is a German surname most notably associated with Olympic track cyclist Toni Merkens.
-
E.
Lemery
Lemery is a coastal municipality in the province of Batangas in the Philippines, known for its commercial activity and proximity to Taal Lake and Volcano.
- 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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e327d12dc08190a5b497692b667ed7 |
completed | April 18, 2026, 6:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c6094e481909aa7402fd17fedae |
completed | May 10, 2026, 8:05 a.m. |
| NEDg | Description generation | batch_6a003dd7e9d481908822da391112eb39 |
completed | May 10, 2026, 8:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a003eb6aa748190b0c8866af405794a |
completed | May 10, 2026, 8:15 a.m. |
Created at: April 10, 2026, 5:09 a.m.