Triple
T1397002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andrott |
E30687
|
entity |
| Predicate | hasCommunicationCodeType |
P27717
|
FINISHED |
| Object | telephone code |
—
|
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: telephone code | Statement: [Andrott, hasCommunicationCodeType, telephone code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommunicationCodeType Context triple: [Andrott, hasCommunicationCodeType, telephone code]
-
A.
hasProgramCode
Indicates that an entity is associated with a specific program identifier or code used to reference or classify it within a system.
-
B.
hasCommunicationFrequency
Indicates how often communication occurs between the related entities.
-
C.
hasContactType
Indicates the specific kind or category of contact relationship that exists between two entities.
-
D.
hasAreaCodeType
Indicates that an entity’s area code is associated with a specific type or classification of area code.
-
E.
communicationMode
Indicates the method or channel through which communication between entities is carried out.
- 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_69a498fd4e408190bd73eca30ea9754c |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3817320819093067b1444d70b74 |
completed | March 1, 2026, 10:53 p.m. |
| PD | Predicate disambiguation | batch_69a4bf017f8081908572121560ec621f |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c13270d8819081d8ee1be34cabf5 |
completed | March 1, 2026, 10:44 p.m. |
Created at: March 1, 2026, 7:59 p.m.