Triple
T13296883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barnala district |
E316706
|
entity |
| Predicate | hasVehicleRegistrationCode |
P1173
|
FINISHED |
| Object |
PB-39
PB-39 is the vehicle registration code assigned to motor vehicles registered in Barnala district in the Indian state of Punjab.
|
E1032647
|
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: PB-39 | Statement: [Barnala district, hasVehicleRegistrationCode, PB-39]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PB-39 Context triple: [Barnala district, hasVehicleRegistrationCode, PB-39]
-
A.
PB-05
PB-05 is the regional vehicle registration code assigned to the Ferozepur district in the Indian state of Punjab.
-
B.
PB-02
PB-02 is the regional vehicle registration code assigned to the Amritsar district in the Indian state of Punjab.
-
C.
BB-39
BB-39 is the hull classification symbol for USS Arizona, a Pennsylvania-class battleship of the United States Navy sunk during the attack on Pearl Harbor in 1941.
-
D.
MB-339 PAN
MB-339 PAN is an Italian jet trainer and light attack aircraft variant widely recognized as the mount of the Frecce Tricolori aerobatic display team.
-
E.
PZ-90
PZ-90 is a geodetic coordinate reference system used by the Russian GLONASS satellite navigation system to define positions on Earth.
- 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: PB-39 Triple: [Barnala district, hasVehicleRegistrationCode, PB-39]
Generated description
PB-39 is the vehicle registration code assigned to motor vehicles registered in Barnala district in the Indian state of Punjab.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PB-39 Target entity description: PB-39 is the vehicle registration code assigned to motor vehicles registered in Barnala district in the Indian state of Punjab.
-
A.
PB-05
PB-05 is the regional vehicle registration code assigned to the Ferozepur district in the Indian state of Punjab.
-
B.
PB-02
PB-02 is the regional vehicle registration code assigned to the Amritsar district in the Indian state of Punjab.
-
C.
BB-39
BB-39 is the hull classification symbol for USS Arizona, a Pennsylvania-class battleship of the United States Navy sunk during the attack on Pearl Harbor in 1941.
-
D.
MB-339 PAN
MB-339 PAN is an Italian jet trainer and light attack aircraft variant widely recognized as the mount of the Frecce Tricolori aerobatic display team.
-
E.
PZ-90
PZ-90 is a geodetic coordinate reference system used by the Russian GLONASS satellite navigation system to define positions on Earth.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990a2f2708190a8f2aa7e7c0b92d2 |
completed | April 11, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716dad3648190bf360955fbdfb2f0 |
completed | May 3, 2026, 9:35 a.m. |
| NEDg | Description generation | batch_69f7177e07508190b46e6a12f09e7986 |
completed | May 3, 2026, 9:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f717e72b988190927b628022bcbf12 |
completed | May 3, 2026, 9:39 a.m. |
Created at: April 9, 2026, 9:28 p.m.