Triple
T30304171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M106 mortar carrier |
E770732
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
M106A1
The M106A1 is an upgraded U.S. tracked armored mortar carrier variant designed to provide mobile, indirect fire support on the battlefield.
|
E1907440
|
NE 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: M106A1 | Statement: [M106 mortar carrier, hasVariant, M106A1]
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: M106A1 Triple: [M106 mortar carrier, hasVariant, M106A1]
Generated description
The M106A1 is an upgraded U.S. tracked armored mortar carrier variant designed to provide mobile, indirect fire support on the battlefield.
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_69f224881b948190b8c4921b250a44a3 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68166930881909608eece2bc5f055 |
completed | May 2, 2026, 10:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a276f0e21d88190870816c1504204b1 |
completed | June 9, 2026, 1:40 a.m. |
| NEDg | Description generation | batch_6a276febe8e48190a61b0e20ac44ab06 |
completed | June 9, 2026, 1:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a27708bfc588190abd7fa5039f5153a |
completed | June 9, 2026, 1:46 a.m. |
Created at: April 29, 2026, 7:49 p.m.