Triple
T13013618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | College Point |
E322484
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object |
Malba
Malba is an affluent residential neighborhood in the northeastern part of Queens, New York City, known for its large waterfront homes and quiet, suburban character.
|
E1015914
|
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: Malba | Statement: [College Point, adjacentTo, Malba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malba Context triple: [College Point, adjacentTo, Malba]
-
A.
Marol
Marol is a prominent residential and commercial locality in Mumbai, India, situated within the larger Andheri suburb and known for its connectivity and mixed urban character.
-
B.
Murlo
Murlo is a small historic municipality in Tuscany, Italy, known for its Etruscan archaeological heritage and picturesque rural landscape.
-
C.
Morong
Morong is a coastal municipality in the Philippine province of Bataan known for its beaches, eco-tourism sites, and the Pawikan (sea turtle) Conservation Center.
-
D.
Morong
Morong is a coastal municipality in the Philippine province of Rizal known for its historic church and proximity to Metro Manila.
-
E.
Marga
Marga is a feminine given name, commonly used as a short or diminutive form of names like Margarita or Margareta.
- 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: Malba Triple: [College Point, adjacentTo, Malba]
Generated description
Malba is an affluent residential neighborhood in the northeastern part of Queens, New York City, known for its large waterfront homes and quiet, suburban character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Malba Target entity description: Malba is an affluent residential neighborhood in the northeastern part of Queens, New York City, known for its large waterfront homes and quiet, suburban character.
-
A.
Marol
Marol is a prominent residential and commercial locality in Mumbai, India, situated within the larger Andheri suburb and known for its connectivity and mixed urban character.
-
B.
Murlo
Murlo is a small historic municipality in Tuscany, Italy, known for its Etruscan archaeological heritage and picturesque rural landscape.
-
C.
Morong
Morong is a coastal municipality in the Philippine province of Bataan known for its beaches, eco-tourism sites, and the Pawikan (sea turtle) Conservation Center.
-
D.
Morong
Morong is a coastal municipality in the Philippine province of Rizal known for its historic church and proximity to Metro Manila.
-
E.
Marga
Marga is a feminine given name, commonly used as a short or diminutive form of names like Margarita or Margareta.
- 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecbb8f4819094d55eb07cb5ad97 |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c11290e08190a41c162d47094203 |
completed | May 3, 2026, 3:29 a.m. |
| NEDg | Description generation | batch_69f6c20a1eb881908a28dc884c2005ef |
completed | May 3, 2026, 3:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6c34b9ec08190bb29458b6f43c388 |
completed | May 3, 2026, 3:38 a.m. |
Created at: April 9, 2026, 8:50 p.m.