Triple
T8523420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naperville Fire Department |
E201750
|
entity |
| Predicate | hasEmergencyPhoneNumber |
P37309
|
FINISHED |
| Object | 911 |
—
|
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: 911 | Statement: [Naperville Fire Department, hasEmergencyPhoneNumber, 911]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmergencyPhoneNumber Context triple: [Naperville Fire Department, hasEmergencyPhoneNumber, 911]
-
A.
hasNonEmergencyNumber
Indicates that an entity is associated with a phone number intended for non-emergency, routine, or informational contact rather than urgent or emergency situations.
-
B.
emergencyPhoneNumber
chosen
Indicates that the object is a phone number designated for use in emergencies or urgent situations.
-
C.
usesEmergencyNumber
Indicates that an entity initiates contact or communication by dialing or otherwise employing an officially designated emergency telephone number.
-
D.
hasEmergencyServices
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
-
E.
hasEmergencyCare
Indicates that an entity provides or is equipped with emergency medical care services for another entity or individuals.
- F. None of above.
Provenance (3 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_69ca8321bb44819081b74df0b710276d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe64362c88190b978a2544eec6e3e |
completed | March 31, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69cbd10f64b4819080859057c19e58f0 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:16 p.m.