Triple
T13232777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | US1667641005 |
E315063
|
entity |
| Predicate | hasNSIN |
P108640
|
FINISHED |
| Object | 166764100 |
—
|
LITERAL FINISHED |
How this triple was built (2 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: 166764100 | Statement: [US1667641005, hasNSIN, 166764100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNSIN Context triple: [US1667641005, hasNSIN, 166764100]
-
A.
hasInn
Indicates that one entity possesses, operates, or is associated with an inn as part of its facilities or properties.
-
B.
hasNationalIdentity
Indicates that an entity possesses or is associated with a particular national identity or nationality.
-
C.
hasINN
Indicates that a pharmaceutical product or substance is associated with a specific International Nonproprietary Name (INN) assigned by the World Health Organization.
-
D.
hasIbnr
Indicates that one entity possesses or is associated with an incurred-but-not-reported (IBNR) amount, typically referring to estimated liabilities for events that have occurred but have not yet been reported.
-
E.
hasWKN
Indicates that an entity is associated with a specific Wertpapierkennnummer (WKN), i.e., a unique German securities identification code.
- F. None of above. chosen
Provenance (4 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d34ff288190bdb550a019b7a470 |
completed | April 10, 2026, 11:52 p.m. |
| PD | Predicate disambiguation | batch_69d98bcb21648190aef241de1e7887e2 |
completed | April 10, 2026, 11:46 p.m. |
| PDg | Predicate description generation | batch_69d98c959ba08190adf29dc0c4e1fca6 |
completed | April 10, 2026, 11:49 p.m. |
Created at: April 9, 2026, 9:22 p.m.