Triple
T12978087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glenn County |
E321581
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object |
Orland
Orland is a small agricultural city in Northern California known for its farming community and rural character.
|
E1012495
|
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: Orland | Statement: [Glenn County, largestCity, Orland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orland Context triple: [Glenn County, largestCity, Orland]
-
A.
Orlando
Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
-
B.
Orlando
Orlando is a common Italian surname borne by numerous individuals, including notable political and cultural figures.
-
C.
Orlando
Orlando is a 1992 British period fantasy film, based on Virginia Woolf’s novel, in which Tilda Swinton plays an androgynous noble who lives for centuries while changing gender.
-
D.
Orlando
Orlando is the Italian literary counterpart of the medieval knight Roland, best known as the chivalric hero of epic poems such as "Orlando Furioso."
-
E.
Orlando
Orlando is a historic township area within Soweto, South Africa, known for its central role in the anti-apartheid struggle and vibrant local culture.
- 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: Orland Triple: [Glenn County, largestCity, Orland]
Generated description
Orland is a small agricultural city in Northern California known for its farming community and rural character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Orland Target entity description: Orland is a small agricultural city in Northern California known for its farming community and rural character.
-
A.
Orlando
Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
-
B.
Orlando
Orlando is a common Italian surname borne by numerous individuals, including notable political and cultural figures.
-
C.
Orlando
Orlando is a 1992 British period fantasy film, based on Virginia Woolf’s novel, in which Tilda Swinton plays an androgynous noble who lives for centuries while changing gender.
-
D.
Orlando
Orlando is the Italian literary counterpart of the medieval knight Roland, best known as the chivalric hero of epic poems such as "Orlando Furioso."
-
E.
Orlando
Orlando is a historic township area within Soweto, South Africa, known for its central role in the anti-apartheid struggle and vibrant local culture.
- 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_69d80763bd6c819094437da5b20b01d2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8f0315c8190aae5908ba65d5867 |
completed | May 3, 2026, 2:54 a.m. |
| NEDg | Description generation | batch_69f6b9dc31ec819093c89ff0a1ccbfa1 |
completed | May 3, 2026, 2:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6baacd7548190af5514923a0dee26 |
completed | May 3, 2026, 3:02 a.m. |
Created at: April 9, 2026, 8:38 p.m.