Triple
T35244133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duncan Sullivan |
E1017611
|
entity |
| Predicate | greenCardType |
P36584
|
FINISHED |
| Object |
United States permanent resident card
The United States permanent resident card is an identification document that grants a non-citizen the legal right to live and work permanently in the United States.
|
E2131030
|
NE FINISHED |
How this triple was built (3 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: United States permanent resident card | Statement: [Duncan Sullivan, greenCardType, United States permanent resident card]
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: United States permanent resident card Triple: [Duncan Sullivan, greenCardType, United States permanent resident card]
Generated description
The United States permanent resident card is an identification document that grants a non-citizen the legal right to live and work permanently in the United States.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: greenCardType Context triple: [Duncan Sullivan, greenCardType, United States permanent resident card]
-
A.
citizenshipType
Indicates the specific legal category or status of an individual's citizenship in relation to a state or country.
-
B.
immigrationType
chosen
Indicates the specific category or classification of an individual’s immigration status or entry into a country.
-
C.
definedCitizenship
Indicates that a formal citizenship status has been legally established or specified for an entity.
-
D.
visaClass
Indicates the specific category or type of visa associated with an individual or immigration case.
-
E.
hasTypicalCitizenship
Indicates that an entity is generally or commonly a citizen of a specified country or jurisdiction.
- F. None of above.
Provenance (6 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_69f76de235048190b990070c23c51b6b |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78f63c8788190b253a18de5ca1312 |
completed | May 3, 2026, 6:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a38042591848190805d4c467c42e382 |
completed | June 21, 2026, 3:32 p.m. |
| NEDg | Description generation | batch_6a380589165c8190b839f07aa93e700b |
completed | June 21, 2026, 3:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a38063b3520819080ba23f3dad18048 |
completed | June 21, 2026, 3:41 p.m. |
| PD | Predicate disambiguation | batch_69f78e2d71248190b850c2802ec170c0 |
completed | May 3, 2026, 6:04 p.m. |
Created at: May 3, 2026, 4:02 p.m.